KR20110043292A - System and method for customizing research - Google Patents

Abstract

PURPOSE: A customizing research system and a method thereof are provided to perform research through the use of a user-customized research tool by directly defining research configuration elements and the relation function among the research configuration elements. CONSTITUTION: A control module(120) comprises the followings. A survey fill-in unit(121) writes out the survey necessary for an online survey and an offline survey. A data manager(122) inputs and manages the data, and an analyzer(123) generates new data by editing response data and synthesizing plural kinds of data. The analysis unit re-classifies the response data, and analyzes the data by executing table analysis and high-level statistic analysis. A report generator(124) writes out a report based on the result which statistically analyzes the response data.

Description

CUSTOMIZED RESEARCH SYSTEM AND METHOD THEREOF {SYSTEM AND METHOD FOR CUSTOMIZING RESEARCH}

The present invention relates to a customized research system and a method thereof, and more particularly, to design online and offline research centered on a user (researcher) rather than a system operator, to design a survey, create a questionnaire, collect data, input data, and process data. And a custom research system and method for executing research using a research tool customized to a user by defining a research-related component and a relation function among the components including analysis and report generation. will be.

In the related art, a number of technologies related to online research methods have been filed in addition to Korean Patent Publication No. 1999-0068294 entitled 'Research Method Using Computer Communication.'

The research method using computer communication may include receiving basic information and life patterns of a subscriber accessing a system for the first time and issuing an ID; Creating a questionnaire when the person who accesses the system is a questionnaire; If the person who accesses the system is a question responder and receives a response by posting a questionnaire according to the information input when the system is joined to each selected panel; Analyzing the data recorded in the questionnaire response and providing the questionnaire to the questionnaire; Reorganizing, integrating, and reanalyzing the surveys according to categories; Selling the already analyzed data and the planned data to the data buyer; And calculating the research expenses and data selling expenses calculated after filling out the questionnaire and obtaining the credit card payment authorization through the Internet.

However, in the conventional research system as described above, the relation function between the components such as the member, questionnaire, response, data processing, analysis output form, etc., which are data generated according to the research progress, is fixedly set. As a result, it was developed as a system designed to be limited to a specific research method, and there was a problem in that it could not satisfy all the needs of individual research system users before the redevelopment of each independent system.

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned problems, and a user (researcher) accessing a research system has a relationship between each component and components related to membership, questionnaires, responses, data processing, analysis, reports, and related research forms. Its purpose is to be able to meet the needs of individual research system users by defining functions directly so that research tools can be modified.

The present invention relates to a customized research system, comprising: a database module for storing research member information, research questionnaire information, research response information, research analysis information, research report information, and data received from the database module; The database module is controlled according to the user's requirements, and the properties of the components and the relation function between the components are determined according to the request signal of the user's computer according to the research progress, and the questionnaire is created online and offline according to the determined user research tool. A control module for executing posting, data collection, input of response data, processing of data and analysis of data, and report generation; Customizing research server comprising a; And a user computer accessing the customizing research server and transmitting and receiving data for receiving various user-centered research services. Characterized in that it comprises a.

Meanwhile, the present invention relates to a custom research method, wherein (a) the custom research server sets the output form, the execution attribute and the execution procedure of each research component, and defines the connection structure, the connection attribute and the execution direction between the research components Registering a configuration; (b) executing, by the customization research server, the survey design by the basic information of the survey factors received from the user computer and the survey design method, and storing the created survey design result; (c) the customization research server creating a questionnaire form (Quform) according to the survey design, defining a basic property and a view property of the questionnaire, and setting the relationship logic between the question logic and the questionnaire, thereby completing the questionnaire creation; (d) the customization research server receiving the collected data and checking a data error; (e) analyzing the data by editing the input data by the customization research server, reclassifying the data, and then analyzing the data by performing a table analysis and an advanced statistical analysis according to an analysis procedure; (F) the customization research server generating and servicing various types of reports or executing a real-time research tool; Characterized in that it comprises a.

According to the present invention as described above, by designing a research server system so that a user (researcher) can set internal functions for each component of the research tool, and set the relationship function between the components, systematic research integrating online and offline There is an effect that can be performed.

In addition, according to the present invention, it can be used as intended by the user using the research system has the effect of increasing the efficiency of the research system.

In addition, according to the present invention, the user can design the research tool in various forms according to the purpose of the research project has the effect of increasing the diversity and expandability of the research.

In addition, according to the present invention, there is no need to newly develop the research system every time according to the needs of the user (researcher), thereby reducing the system development cost.

Specific features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. In the meantime, when it is determined that the detailed description of the known functions and configurations related to the present invention may unnecessarily obscure the subject matter of the present invention, it should be noted that the detailed description is omitted.

Hereinafter, with reference to the accompanying drawings will be described in detail the present invention.

The customizing research system and method thereof according to the present invention will be described with reference to FIGS. 1 to 16.

1 is a block diagram conceptually illustrating a customizing research system S according to the present invention, and includes a customizing research server 100, a payment server 200, and a user computer 300 as illustrated.

The customized research server 100 interprets the data received from the database module 110 and the database module for storing research member information, research questionnaire information, research response information, research analysis information, and research report information, and interprets the data received from the database module. Therefore, the database module is controlled, and according to the progress of the research, the properties of the components and the relation functions between the components are determined in accordance with the request signal of the user computer 300, and the questionnaire is applied online and offline according to the determined user research tool. The control module 120 executes the preparation and posting, data collection, input of response data, processing of data and analysis of data, and report generation.

Specifically, as illustrated in FIG. 2, the database module 110 may include a customer member requesting a research, a researcher member performing a research, an interviewer member in online and offline surveys, a respondent member responding to a survey, and response data. Member database unit that stores the data editor member who verifies and edits, the data inputter member who inputs the response data of the offline survey, the personal details of the data analyst members who process and analyzes the response data, and the research participation information. (111), a survey form created for users to conduct research projects, a standard survey library that supports various types of standard surveys, a common survey library shared by users for use by multiple users, and frequently used by users. A user library that collects surveys A questionnaire database 112 configured to be configured, a response database unit 113 which functions to store collected data by respondents responding to a questionnaire or inputters input response data, and stores processing information and analysis information of the response data; And an analysis database 114 having a function of storing a report, and a report database 115 having a function of storing report preparation information.

In addition, the control module 120 includes a questionnaire preparation unit 121 that executes a process for creating a questionnaire required for an online survey and an offline survey, and a respondent responds to the survey or inputs response data through an online survey and an offline survey. A data management unit 122 that collects data by inputting or executes a process of collecting, inputting, and managing data such as error checking, correcting, deleting, and storing the collected response data; and editing response data and adding multiple data; After analyzing data such as generating new data by merging or reclassifying the response data, and performing table analysis and advanced statistical analysis, the analysis unit 123 for analyzing the data and statistical analysis of the response data Report generation unit 124 that executes the report creation process based on the results and automatic investigation design process according to the set conditions It includes an automatic survey design department 125 is running.

More specifically, the survey creator 121 creates a questionnaire intended by the user with reference to a plurality of survey libraries stored in the survey database 112 or uploads a word questionnaire file created by the user to create a new questionnaire or a user. Create a new survey by uploading an Excel questionnaire file created by the user, or create a questionnaire by adding items one by one by selecting from 16 types manually, defining the basic and view properties of the questionnaire, and the relationship logic between the questionnaires. Set the function to insert the image, sound, and video into the survey, and show the order of the survey items differently for each respondent to prevent skewed responses to predecessors or remove unsatisfactory responses. To set up a survey to branch based on respondents' responses Perform the function of setting the survey piping that the response of the previous questionnaire is applied to the next questionnaire and outputting it on the screen, setting the condition of the questionnaire respondents, or allocating the response quota amount for a specific condition, It performs the function of checking the questionnaire response process for the entire questionnaire and the function to modify the questionnaire to the form desired by the user.

In addition, the survey creator 121 performs a function of automatically generating work data, which is a repository of response data associated with a questionnaire, and automatically generates a variable of the work data and a user's request. This function modifies the survey variable accordingly.

In addition, the survey preparation unit 121 converts the completed survey into a web questionnaire file for online research and publishes it on the web, transmits the web questionnaire file to an e-mail of a respondent, and distributes the web survey file for mobile survey, Copy and distribute the questionnaire file to the interviewer's computer, which is a mobile notebook, or convert it to a web questionnaire file for telephone surveys, and then transfer the web questionnaire file to the telephone interviewer's computer for distribution, or for offline surveys. It converts the word questionnaire into a printer and outputs it to the printer for distribution to the interviewer.

In addition, the data manager 122 receives data from the respondent computer in response to the web questionnaire file posted online on the web, and responds to the web questionnaire file distributed to the respondent by e-mail. Receives input from the interviewer computer through the interview with the interviewer and the respondent in the web questionnaire file of the interviewer's computer, which is a mobile notebook distributed for mobile notebook research, and is input for the interview. The telephone interviewer directly inputs the responses of the respondents to the completed web questionnaire file, thereby receiving this response data through the telephone interview computer, and responding to the paper questionnaire distributed directly to the respondent by the interviewer for offline surveys. Enter the details directly into the input form of the survey response data As a result, the response data is input from the expert inputter computer.

In addition, the data management unit 122 performs a function for checking the response data and response frequency for each respondent, controls the response by the response quota of the questionnaire quota or performs a function for confirming the amount of data for each respondent or inputter, The function of inputting collected data from offline surveys, respondents responding from online surveys, or telephone interviewers responding from telephone surveys, and checks the validation of data collected online and offline. In addition, the respondents are identified from the collected data to classify respondents according to their reliability, find and classify the pattern of survey responses to respondents, and analyze the reliability of the survey responses to determine the reliability of respondents. To restrict participation in the survey.

In addition, the analysis unit 123 edits the input data, classifies, codes, and reclassifies the free-form responses and other question responses, and processes the data for analysis, and adds and merges multiple data. It creates a new data by using and performs the function of analyzing data by executing table analysis and advanced statistical analysis according to the analysis procedure.

In addition, the report generation unit 124 generates various types of reports on the results obtained by executing data processing and data analysis process, or the user provides a survey design, questionnaire, data, analysis program, analysis result, report, etc. All research performance information and result information can be created and provided online, or the user can reorganize the research component including the research procedure and contents of each research step to meet the customer's needs. We provide customized research tools to directly perform all procedures such as and report generation.

In addition, the payment server 200 is a financial institution server for the user to pay online for the use of the research tool provided by the customized research server 100.

In addition, the user computer 300 connects to the customizing research server 100 through the information communication network N and transmits and receives data for receiving various user-centered research services.

The customizing research method using the customizing research system S according to the present invention will be described with reference to FIGS. 3 to 16 as follows.

As shown in FIG. 3, the customizing research server 100 registers a research configuration by setting an output form, a performance attribute, and a procedure of each research component, and defining a connection structure, a connection attribute, and a direction between each research component. The first step (S100), the second step of performing the survey design by the basic research information and the survey design method received from the user computer 300, the customization research server 100 to store the created survey design results ( S200), the customizing research server 100 creates a questionnaire (Quform) according to the survey design, by defining the basic properties and view properties of the questionnaire, and set the relationship logic of the question logic and questionnaire, to complete the questionnaire 3 process (S300), after publishing and distributing the questionnaire according to the purpose of the survey, the customizing research server 100 receives the collected data and receives the input data. The fourth step (S400) of error checking of the data, the customizing research server 100 edits the input data, reclassifies the data, and then analyzes the data by performing table analysis and advanced statistical analysis according to an analysis procedure. The fifth step (S500) and the customizing research server 100 includes a sixth step (S600) of generating and servicing various types of reports or executing a real-time research tool.

The research configuration registration process of the first process S100 of FIG. 3 will now be described in detail with reference to FIGS. 4 and 5.

As illustrated in FIG. 4, the automatic research design unit 125 of the customizing research server 100 control module 120 includes research components such as member data, design data, questionnaire data, response data, external data, analysis data, and reports. The list is transmitted to the user computer 300 (S110).

Thereafter, the automatic research design unit 125 arranges the research components according to the entire research process from the user computer 300 to survey design, questionnaire preparation and publication / distribution, data collection and error inspection, data processing and data analysis, and report generation. Receiving the on-line and off-line research work order determination information, and receives the research condition and environment setting information to be executed in each research step (120).

Subsequently, the auto-search design unit 125 sets a property of each component and a relationship between the components, registers them in the research, and generates a research tool configured to be executed according to a research procedure designed by the user (S130). ).

In this case, as shown in FIG. 5, in the process of setting property of a research component such as design data, questionnaire data, response data, external data, analysis data, report, and the like, setting an output form that is an example of a component execution result is set (S131). And setting an execution attribute which is an option for executing the component (S132) and setting an execution procedure corresponding to the execution method of the component (S133). In addition, the relationship setting process between each component, the connection structure setting step (S134) means the connected form between the components, the connection property setting step (S135) for determining the movement of the result between the components and unidirectional, bidirectional, iterative performance, etc. Execution direction setting step (S136) of determining the execution direction of the components of the.

Referring to FIG. 6, the survey design process of the second process S200 of FIG. 3 will be described in detail as follows.

As shown in FIG. 6, the automatic survey design unit 125 includes survey project title information, offline survey, online survey, e-mail survey, mobile laptop survey, and telephone survey survey information selected from the user computer 300; Research subject information selected from region, age, gender, specific conditions, research field and item information selected from manufacturing, information and communication, finance, service, public administration, etc., concept development, product test, advertisement, brand, corporate image, Receive survey basic information such as demand forecast, marketability, distribution, price, customer satisfaction, social public opinion, consulting, and other survey type information selected from the group (S210).

Thereafter, the automatic survey design unit 125 receives the survey design condition from the user computer 300 (S220).

Specifically, the automatic survey design unit 125 is a stratified sampling method, a cluster sampling method, a purposive quarter sampling method, a random sampling method, a random sampling method, and a system sampling method from the user computer 300. Receives the sampling method information selected from (Systematic Sampling), and receives the sampling method information selected from Proportional Allocation, Neyman Allocation, Purposive Quota. In addition, the automatic survey design unit 125 performs the research member from the user computer 300 according to the survey method selected in the foregoing process, the researcher member who responds to the research, the interviewer member of the offline survey, the telephone interviewer of the telephone survey. Receive personal details and research participation information of members, inputter members who input response data of offline surveys, and data analyst members who process and analyze response data. In addition, the automatic survey design unit 125 receives the information of the entire survey period from the user computer 300, and survey progress process information such as questionnaire preparation, surveyor training, due diligence preparation, data collection, data processing, data analysis, reports, etc. Receive detailed survey schedule information.

Thereafter, the automatic survey design unit 125 executes the survey design by operating the survey design engine (S230).

In addition, the automatic survey design unit 125 stores the survey design result executed by the survey design engine in the questionnaire database unit 112 (S240). In detail, the automatic survey design unit 125 distributes the number of samples according to the survey target conditions by the sample distribution method selected according to the survey design and stores the number of samples in the questionnaire database unit 112. In addition, the survey subjects (persons, households, businesses, etc.) are extracted from the respondent member database by the sampling method selected according to the survey design and stored in the questionnaire database unit 112. In addition, according to the survey design, survey interviewers are arranged, and the survey table is generated as many as the number of samples allocated to each survey interviewer and stored in the questionnaire database unit 112. In addition, the response data inputter selected according to the survey design is stored in the questionnaire database 112. In addition, the data analyzer selected for processing the response data according to the survey design is stored in the questionnaire database 112. In addition, a survey progress process and detailed survey schedule set according to the survey design are generated and stored in the survey database unit 112.

The questionnaire creation process of the third process (S300) of FIG. 3 will now be described in detail with reference to FIG. 7.

As illustrated in FIG. 7, the questionnaire creator 121 of the customizing research server 100 control module 120 may select a questionnaire of any form among a new questionnaire form, an existing questionnaire form, an external questionnaire file, and a questionnaire library. The menu screen is transmitted to the user computer 300 (S310).

Thereafter, the survey preparation unit 121 determines whether the question form selection signal received from the user computer 300 is a new question form opening selection signal (S320).

As a result of the determination in step S320, when the selection form opens a new questionnaire form, the questionnaire preparation unit 121 generates a new questionnaire form, stores it in the questionnaire database unit 112, and transmits the new questionnaire form to the user computer 300 (S330). ).

Thereafter, the survey preparation unit 121 generates working data (S340). Here, working data is a data structure that constitutes the data obtained through survey or measurement and observation, and includes project information, unique work data ID, work data name, question form ID indicating connected question form, and response data. Basic information about the operation data such as the number of variables representing the number of variables, the number of responses representing the case of response data, and the number of data sets that form a set by dividing the response data into a certain size when the number of variables is large. Also, the variable constituting the column of response data in response to the questionnaire, the variable value describing the individual response value in response to the questionnaire view of the questionnaire, the respondent or the respondent answered the questionnaire Alternatively, the response data may include response data in which actual data is stored.

Thereafter, the questionnaire preparation unit 121 receives the questionnaire information which is a basic property from the user computer 300 and stores it in the questionnaire database unit 112 (S350).

That is, the questionnaire preparation unit 121 displays 16 questionnaire type information such as selection type, evaluation type, free answer type, total type, block type, and fingerprint type, and a question item writing screen of the selected questionnaire type on the screen of the user computer 300. Will print to

Accordingly, a user may enter a survey attribute that has the characteristics of a questionnaire or express the survey information, enter a survey view, which is a basic item that is provided as an example for respondents to select, enter a survey directive and a survey abbreviation, Enter the survey response range or unit, the survey response length, set image and video files of the user's computer in the survey attribute or the survey view, set various forms and output forms, or fix the order of the survey view or the survey attribute. For example, the configuration information of the questionnaire items, such as setting a rotation that can be displayed in a certain proportion or completely randomly to prevent an unsatisfactory response of the respondents, is input.

Thereafter, the questionnaire preparing unit 121 sets the questionnaire context by receiving the view property and the relationship logic between the question logic and the questionnaire from the user computer 300 (S360).

That is, the questionnaire preparation unit 121 sets the questionnaire view logic so that the display form of the questionnaire is output differently according to a condition when the respondent responds to the questionnaire or when the inputter inputs data. Control the display of the questionnaire, limit the response according to the conditions in the questionnaire, control the response and input process by setting the response and input logic to enable specific input, or control special functions. Control the execution of the questionnaire, or set up the data check logic of the questionnaire response and input data to check the logic of the questionnaire entered by the user, such as performing error check of the data. Received from the 300 and stored in the questionnaire database 112.

In addition, when the responder responds to the questionnaire or when the inputter enters data, the questionnaire preparation unit 121 determines and provides the questionnaire within the single questionnaire item, according to the questionnaire's own property such as the questionnaire attribute or the questionnaire view. Extended question logic, question branch conditions, question piping, such as questionnaire property logic, conditional relationships between questionnaires and questionnaires in multiple questionnaires, or complex structural conditions added to perform special functions (Piping), the question rotation (Rotation) condition, the questionnaire (Quota) conditions, such as setting the user's questionnaire Logic (Logic) received from the user computer 300, the questionnaire database unit 112 ).

Thereafter, the survey preparation unit 121 generates an automatic variable (S370).

That is, the questionnaire preparation unit 121 receives the questionnaire item information of the questionnaire form from the questionnaire database unit 112, receives the variable information of the job data from the response database unit 113, and then the user from the user computer 300. Receives information such as survey type, survey attribute, and number of responses of the questionnaire prepared by the user, compares the variable name of the questionnaire with the entire list of variables in the work data, automatically creates a new variable with the next variable name, and sets the questionnaire information. The generated variable information is stored in the response database 113.

Thereafter, the survey preparation unit 121 creates a preview of the survey form and transmits it to the user computer 300 (S380).

That is, when the questionnaire creation unit 121 receives the question form preview selection signal from the user computer 300, the questionnaire preparation unit 121 receives the questionnaire form information from the questionnaire database unit 112 and the questionnaire form from the response database unit 113. Receives one-to-one job data information and completes questionnaire information, questionnaire attribute information, questionnaire view information, questionnaire display, questionnaire, association logic between questionnaires, questionnaire context including questionnaire, etc. The questionnaire information is combined with the variable information of the job data connected thereto to create a comprehensive questionnaire form and transmitted to the user computer 300 so that the user can simultaneously understand the structure of the questionnaire and the job data.

Thereafter, the questionnaire preparation unit 121 stores the questionnaire form (S390).

That is, when the questionnaire creation unit 121 receives the selection signal for storing the questionnaire form after completing the questionnaire form from the user computer 300, the questionnaire basic attribute information such as question items, question attributes, and the view of the questionnaire form and Receive all the survey information of the survey context information such as questionnaire, survey quarter, survey piping, survey logic, and store in the survey database unit 112, all the variable information and response data information of the work data associated with the stored survey form Received and stored in the response database 113.

On the other hand, when the determination result of step S320, when the new questionnaire form open selection signal is not, the questionnaire preparation unit 121 determines whether the existing questionnaire form open selection signal (S391).

As a result of the determination in step S391, when the questionnaire form opening selection signal is received, the questionnaire preparation unit 121 receives an existing questionnaire form list from the questionnaire database unit 112 and transmits it to the user computer 300 (S392).

The questionnaire creation unit 121 receives an existing questionnaire form selected from the user computer 300 among the existing questionnaire form list, generates it as a new questionnaire form and stores it in the questionnaire database unit 112, and then the user computer 300. To transmit (S393). Thereafter, the survey preparation unit 121 performs the procedure to step S340.

On the other hand, if it is determined in step S391 that the questionnaire form open selection signal is not, the questionnaire preparation unit 121 determines whether the external questionnaire file selection signal (S394).

As a result of the determination in step S394, when the external questionnaire file selection signal is received, the questionnaire preparation unit 121 receives an external questionnaire file such as a word and an Excel format from the user computer 300, and loads the questionnaire information from the external questionnaire file. After (S395), the section information is analyzed and classified into questionnaire type, questionnaire content, view item, attribute item, and the like, the questionnaire information is generated, converted into a questionnaire form, stored in the questionnaire database 112, and then the user computer ( 300) (S396). Thereafter, the survey preparation unit 121 performs the procedure to step S340.

On the other hand, if the determination result of step S394, the external questionnaire file selection signal, the questionnaire preparation unit 121 determines whether the questionnaire library open selection signal (S397).

As a result of the determination in step S397, when the questionnaire library selection signal is selected, the questionnaire preparing unit 121 receives the questionnaire library selected by the user from the questionnaire database unit 112 (S398), converts it into a questionnaire form, and converts it into a new questionnaire form. After generating and storing in the questionnaire database 112, and transmits to the user computer (300) (S399). Thereafter, the survey preparation unit 121 performs the procedure to step S340.

For reference, the question form (Quform) refers to the form that forms the entire questionnaire for the survey, project information and unique questionnaire form ID, questionnaire form name, survey method, survey area, sample size, sampling method, survey start date, survey It contains basic information on the survey form, such as the end date, and also includes the questionnaire (Question), the questionnaire attribute (Attribute), the questionnaire (Example), the questionnaire logic (Logic), the questionnaire (Quota).

Here, the survey library (Library), the user has reclassified the survey form according to the survey categories, such as survey type, survey method, survey items, survey details, survey period in order to easily reuse the existing survey form As a (QuLibrary), the user can take the entire questionnaire form or a part of the questionnaire in the questionnaire form and generate the user's questionnaire form. It includes a standard survey library that is categorized into survey categories in various formats, a public survey library that is shared for all users, and a user survey library that collects frequently used surveys by individual users. .

After the questionnaire distribution, which is the fourth process (S400) of FIG. 3, a process of receiving data is described in detail with reference to FIG. 8.

When the questionnaire created through the third process (S300) is converted into a web questionnaire file for online survey and published on the web, when the web questionnaire file is transmitted by a respondent's e-mail and distributed, the web questionnaire file is interviewed. When distributing through a mobile notebook of a circle, converting a web questionnaire file for a telephone interview for distribution, and distributing it through a telephone interview computer, outputting a paper questionnaire for offline survey, and distributing it to an interviewer. And distributed.

That is, when the questionnaire composition unit 121 converts the web questionnaire file into an online survey and publishes it on the web by connecting to a web page, the questionnaire preparation unit is distributed by transmitting the web questionnaire file to the responder's e-mail. When the 121 copies the web questionnaire file to the interviewer computer which is the mobile notebook and distributes it to the interviewer computer that is the mobile notebook so that the respondents input the web questionnaire file, the survey preparing unit 121 sets up the web for telephone survey. Convert to a questionnaire file and send it to the telephone interviewer's computer, and distribute the telephone interviewer to enter the web questionnaire, convert it to a word questionnaire file for offline surveys, print it as a paper questionnaire, and distribute the interviewer directly to the respondent. Surveys can be published and distributed according to various research purposes, such as

Subsequently, the data management unit 122 receives data responded by the respondent to the web questionnaire file posted on the web online from the user computer 300 (S410), and the data management unit 122 distributes the web questionnaire by e-mail. In response to the data received by the respondent from the user's computer 300 (S420), the interviewer responded to the web questionnaire file of the interviewer computer, which is a mobile notebook, through the interview with the interviewer and the data manager 122 Is received from an interviewer computer which is a mobile notebook (S430), and the telephone interviewer directly inputs the data responded by the respondent to the web questionnaire file distributed for the telephone survey, so that the data manager 122 calls the response data. Received input from the interviewer computer (S440), the paper questionnaire distributed directly to the respondents by the interviewer for offline investigation, the professional input the contents of the respondents By the direct input (puncher), the data management unit 122 receives the response data from these professional party computer input (S450). All such response data is stored in the response database 113.

Subsequently, the data manager 122 responds to the on-line and off-line state through the foregoing process and performs an error check (Validation check) of the inputted response data (S460).

In more detail, as shown in FIG. 9, the step S460 is a process of checking whether the response data and the user-created question form and question logic match the log data. Receives a question form (Quform) and a question logic (Logic) from the questionnaire database unit 112 (S461), and receives the variable information and response data of the job data connected to the selected questionnaire form from the response database unit 113 (S462). ).

In step S461 and step S462, the data management unit 122 basically based on the received questionnaire form, question logic and variable information, questionnaire contents and response data for each question item, and according to its own attributes for each question item type. The logic provided is checked (S463).

That is, the data management unit 122 checks the non-response of the questionnaire of single or multiple responses, checks the numerical questionnaire of the numerical questionnaire, accepts range or reject range of a specific number or character string, The questionnaire property logic such as the sum match test of the sum questionnaire and the duplicate response check of the multi-response questionnaire is examined.

In addition, the data management unit 122 is a relationship condition or a special condition between the various surveys and the questionnaire, the non-response check of the question between the specific questionnaire and the moving target questionnaire by the questionnaire, the acceptance value (Accept Value) test between the questionnaire or The questionnaire test logic (Extended Logic), such as a check (Reject Value) test is checked (S464).

Subsequently, the data manager 122 transmits an error check result to the user computer 300 (S465), and receives the modified response data according to the response data from which the error has occurred from the user computer 300. Store in (S466).

In this case, the data manager 122 may perform a check on the response data for some respondents or an error check on the response data for all respondents.

For reference, the data manager 122 classifies respondents according to the reliability by identifying response tendencies from the collected response data, finds and classifies the pattern of questionnaire responses to the respondents, and analyzes the reliability of the questionnaire response. By judging the reliability of the respondents, the role of restricting participation in the survey is to be limited. Respondents participating in the survey are surveyed by using the respondents 'survey response pattern information stored in the response database 113 to identify the respondents' survey response patterns such as no response rate, participation rate, similar response rate, and unsatisfactory response rate. According to the classification of respondents, and whether the determined respondents' ratings are reliable and appropriate, the respondents should participate in the survey only if they meet the appropriate ratings. The data manager 122 receives the completed information on the questionnaire and checks the response pattern of the questionnaire participated by the respondent, and then stores the responder questionnaire response pattern in the response database 113 to be used for future surveys.

The data processing and data analyzing process, which is the fifth process S500 of FIG. 3, will be described in detail with reference to FIGS. 10 to 15.

As shown in FIG. 10, the analysis unit 123 is responsive to the online and offline phases through the foregoing fourth process S400 to edit the input response data and variable information (S510), and the analysis unit 123 In the input data, the long text response data is divided into short sentences or other text response data are classified (S520). In addition, the analysis unit 123 classifies and classifies the text or numerical data into a similar group from the input data, or regroups them by grouping them again (S530). In addition, the analysis unit 123 performs data analysis, such as table analysis or advanced statistical analysis, on the response data that has undergone the data processing step according to an analysis program created by the user in accordance with an analysis procedure (S540).

In addition, step S510 is a step of editing data and variable information collected in an online and offline survey, and as shown in FIG. 11, the analysis unit 123 transmits variable information to the user computer 300 (S511). The variable name, variable label, variable value, variable value label, etc. are received from the user computer 300, and the variable information of the job data is changed, and the variable information of the job data is changed and stored in the response database 113 (S512). ).

Thereafter, the analysis unit 123 receives the entire response data corresponding to the variable information of the job data from the response data unit 113 and transmits the response data corresponding to all the variables for all the respondents to the user computer 300 by the grid sheet. (S513), the changed response value is received from the user computer 300 and applied to the response data (S514).

Thereafter, the analysis unit 123 edits the data using the command syntax (S515).

That is, the analysis unit 123 is a statement that edits a variable label, a Var statement, a value statement that edits a label of a variable value, a statement that creates a new variable or copies data of an existing variable into a new variable as it is. Command Syntax such as Compute statement, If statement that processes data of variable according to condition of variable value, Recode statement that changes or replaces specific variable value with other value, etc. by using Scripter Engine It analyzes and replaces the variable or data with a statement that processes each command syntax, and executes the converted statement so that it can be processed. Then, the response data or variable of the actual work data is edited and the response database unit 113 is processed. Save it.

Thereafter, the analysis unit 123 merges several response data by adding all or part of response data and variables of different job data as columns or adding them as a case (S516).

That is, when the analysis unit 123 adds multiple response data to the column, the response case of each response data that matches the value of the matching variable selected by the user through the user computer 300 is copied to fit the newly created variable. If you add a column and add multiple response data as a case, each response data is added as a new case according to the matching variables selected by the user in each column.

In addition, the step S520 is a step of dividing the long text response data into short text response data from the data collected on-line and off-line, and as shown in FIG. Receive text data of the respondent who has answered the long questionnaire from) and transmit the text data to the user computer 300 with the questionnaire content (S521), where the '/' character is located at the position where the text data is semantically divided. By input, the content indicating the position where the palm print text is divided is received from the user computer 300 and stored in the response database 113 (S522).

Subsequently, the analysis unit 123 generates as many new variables as the number of long texts are divided into short texts, and assigns the divided texts to columns of response data of newly created variables, respectively. (S523), the long response data of all respondents divided by the user computer 300 are separately assigned to the columns of the response data of the variable newly generated by the analysis unit 123, and the response database Stored in the unit 113 (S524).

In addition, the step S530 is a step of classifying and classifying text data or numerical data obtained by the free form response or other question response from the data collected on-line and off-line into a similar group, coding or re-grouping, and reclassifying some data. In the middle of collecting data, it is possible to repeatedly classify and code text data or numerical data into similar groups by using some inputted response data, and to program the recording operation in advance to record the final completed response data. Provides the ability to batch work quickly.

As shown in FIG. 13, the analysis unit 123 receives the work data selected by the user from the response database 113 (S531), and among the variables of the working data, selects a variable to be recorded. In step S532, a recording task for recording text data is received.

The analysis unit 123 obtains a text data list of the selected variables from the response data and transmits the text data list to the user computer 300 in operation S533.

Subsequently, the analysis unit 123 receives a selection signal according to transmission of a text data list of any one of a new text recording selection signal, an external codebook file recording selection signal, and an open bank coding selection signal from the user computer 300, and accordingly Create a new codebook and configure a code set (S534).

That is, in the case of a new text recording selection signal, the analysis unit 123 generates a new code book, and when the user adds representative codes inputting code values and labels to classify texts, the sub code is empty. In the case of configuring a code set and an external codebook file recording selection signal, the analyzer 123 loads a codebook file of an MS Excel format to generate a new codebook, and then uses the representative code and subcode of the loaded external codebook. In the case of configuring a code set and selecting an open bank recording signal, the analyzer 123 reads an open bank codebook to generate a new codebook, and then uses the representative code and subcode of the read codebook. To construct a code set.

Subsequently, the analysis unit 123 receives the information classified as the lower code from the user computer 300 or the analysis unit 123 by selecting a text list that is semantically the same concept or contains similarity and adding the same to the representative code. Automatically classify according to the representative code and sub-code of the codebook generated by loading directly (S535).

The analysis unit 123 classifies all the text lists of the response data according to the codebook, and then executes a recording process so that all the texts of the response data are coded and converted according to the classification scheme of the codebook, and the response data is returned to the response database unit 113. In the case of storing the codebook, the codebook may be stored as an external codebook file in MS Excel format or stored in an open bank (S536).

Thereafter, the analysis unit 123 performs the procedure in step S532 to record additionally investigated response data into an existing codebook or repeatedly recode the previously recorded response data.

For reference, data recording may include classifying and encoding text data or numerical data into similar groups in the data collected by the custom research server 100 online and offline, and reclassifying the data. Refers to the data processing stage.

This includes a text data recording step for recording according to the type of data and a numerical data recording step, a multivariate recording step for batch recording multiple variables simultaneously, a multi-layer recording step for recording according to a hierarchy, and a conditional logic recording for a conditional recording. The method may include a step of constructing a code book in which information classified by grouping texts or numerical values is classified, and storing the code book in an external file form.

In this case, the text data recording and the numerical data recording step may perform text data recording in the case of a character type according to the type of collected data, and perform numerical data recording in the case of a numeric type. When the user selects a variable and starts the recording step, the custom research server 100 receives a response value list in which counts are calculated for each response value of the selected variable from the response data and converts it into a code base that is a basic unit. Done. The converted codebase list is displayed on the user screen. Then, the user creates a representative codebase, selects similar codebases, and puts them into a subcodebase within the generated representative codebase. In addition, the user can freely move the codebase to another representative codebase as intended. In this way, the user classifies all codebases into the corresponding representative codebase and assigns a unique code value of each representative codebase. And finally, when the recording is executed, the customizing research server 100 changes the response data of the variable into a code structure classified by the user. At this time, the data recording of the variable includes an update type recording in which the response data is changed by recording the selected variable as it is, and a New type recording in which the response data recorded in the generated new variable is copied. In addition, the user can sort the codebase or search for and classify words or sentences that contain the same concept or similarity in terms of meaning, so that the recording process can be processed quickly.

In addition, the Multi Variable Recoding step refers to batch recording of several variables at the same time. When the user selects several variables having a similar concept and starts the recording step, the customized research server 100 responds with the response data. Receive a list of response values for counting the response values of the selected variables from the variable and convert them to the code base (Code Base). If you classify this and execute recording, the selected variables are collectively recorded in the same classification system.

In addition, in the multi-layer recording step, when a user classifies data, it is repeatedly reclassified. The multi-layer recording process is divided into re-layering layers. So, single layer recording refers to a codebase state where variables are converted from the response data into the codebase and are not yet classified in one layer. Dual-layer recording refers to recordings that first classify variables in two layers, classifying the codebase in the first layer as the representative codebase in the second layer. Triple-layer recording refers to recordings that classify variables second in three layers, first classifying from first layer to second layer, and then reclassifying them from second layer to third layer. The customization research server 100 provides a user screen to perform triple layer recording. Multi-layer recording refers to recording that repeatedly performs such dual or triple layer recording.

In addition, in the conditional recording step, when the user classifies the data, the classification condition may have a complex meaning or it is difficult to classify the text. At this time, the custom research server 100 It is easily classified by conditional logic such as AND and OR.

In addition, the code book creation step stores the code structure in which the user classifies the data in an MS Excel file format, thereby maximizing the reusability of the code classification system. When response data is added or similar surveys are performed repeatedly, these codebooks are imported and sorted into classified code systems, and some of the unclassified items are further classified and reused by upgrading the codebook continuously.

In this case, the code book refers to an object that organizes information classified by grouping texts or numbers. The code book is a code structure composed of several code sheets, and the recording process is performed on various variables from response data in the recording step. As a result, several code sheets are generated from a single response data, and the codebooks are composed by combining these multiple code sheets into one. This includes a code base, a code set that binds the code base, a code sheet that binds the code set, which is the most basic unit of recording, as shown in FIG. 14; It includes.

The code base is the most basic unit of recording, and is composed of a unique code, a code value, a code label, a code count, and the like. In the recording step, the codebase is constructed by using the response value, the label, and the calculated count of the variable from the response data. The codebase in the codebase is numerical or text depending on the type of the response data. If the response data is text, the text data is assigned to the code value. If the response data is a numerical value, the numerical value is assigned to the code value and the response value label is assigned to the code label. In addition, the count calculated from the response data is substituted into the code count.

A code set is a code structure made up of a single key code base by combining several code bases that have semantically identical concepts or similarities. It consists of one codeset, including four subcode bases. The code value of the representative codebase is the only value that does not overlap, and the code value and code label of the representative codebase are basically replaced by the code value and code label of the most frequent codebase at the time of creation, and this can be changed by the user. The code count of the representative codebase is the sum of the counts of the lower codebases.

A code sheet is a code structure composed of several code sets. In a recording step, a code sheet is constructed by using response values of variables from response data.

In operation S540, as shown in FIG. 15, the analysis unit 123 collects the table analysis tasks in various tabular forms suitable for the report format with respect to the input data collected online and offline. (S541), and create an advanced statistical analysis tasks such as frequency analysis, cross analysis, difference comparison analysis, analysis of variance, correlation analysis, regression analysis, categorical data analysis, factor analysis, discriminant analysis, colony analysis (S542). The analyzing unit 123 executes the analysis (S543).

At this time, the data analysis includes an analysis program and a working data. In addition, if a questionnaire form exists, the questionnaire form may be connected to the analysis program, and thus information of the questionnaire is provided together with the result of the table analysis or the advanced statistical analysis.

The analysis program is an object that has programmed a procedure for analyzing data. The analysis program includes an analysis task, an analysis task and an analysis task for processing job data.

The analysis task is a basic unit for analyzing data and includes an analysis routine and analysis contents. These include table analysis task, frequency analysis task, cross analysis task, difference comparison task, variance analysis task, correlation analysis task, regression task, categorical data analysis task, factor analysis task, colony analysis task, discriminant analysis task, etc. It includes.

Analyze Job refers to an object that processes analysis tasks and job data, which includes unique analysis job IDs, analysis job names, job data to be analyzed, and analysis scripts (programs to modify and edit job data). Script), which contains the basic analysis tasks. Here, one analysis job is connected one-to-one with one job data, and one analysis program includes several multi-analyze jobs. Thus, each analysis task can be linked to different task data.

In the step of executing the analysis program, when the user executes the analysis task, the customization research server 100 loads the task data set by the user in the analysis task. After that, the analysis script written by the user is executed. The command syntax is parsed by the analysis scripter engine, and the modification and editing of the work data is processed according to individual processing procedures. Similarly, the user executes the analysis task checked by the user, and the data analyzer engine analyzes the work data according to the analysis type and the setting of each analysis task, and then generates a predetermined result for each analysis task. Save the form and print it out to the user.

The table analysis step is a step of creating a report tabular table from a base table to a complex aggregate table on data collected by the custom research server 100 online and offline. Create a Cross Table). The customizing research server 100 provides a user interface so that a user can easily and conveniently create a table. The user selects the Row and Column variables of the individual table analysis task by Drag-Drop method, and freely changes the addition, deletion, or movement of variables, and the frequency, percentage, and mean for each variable. , Top n percent like Top2% and Top3%, bottom n percent like Bottom2% and Bottom3%, Variance, Standard Deviation, Coefficient of Variation, Standard Error, Minimum Set statistics such as minimum, maximum, median, and mode. It also makes it easy to add or delete table analysis tasks, move or duplicate them, and set row and column variables for a table analysis task in a batch, or set options such as table types, statistics, or conditions for a table task. It provides user-friendly interface to configure. In addition, the table analysis task, in particular, when a questionnaire form exists, when the user selects and loads the questionnaire form, all questionnaire items of the questionnaire form are automatically set with variables and statistics related to the questionnaire according to the questionnaire type. Because it is converted to a task, it is easy to create an automation table that fits the survey form. In addition, when a user creates a complex table as well as a base table, a simple setup is performed without converting data such as adding a case or merging variables or generating new data. You can easily create complex tables of your choice. All tables are created by the Table Base, which is the basic unit of the Cross Table by Row and Column variables. The table base includes table titles, row / column variable information, frequency of row / column variables, cross cell statistics, and row / comumn margin statistics. Tables are intersected, merged, separated, and nested by parent table base and child table base, which form a hierarchical tree structure of the table, to create various types of tables. .

Here, the type of table includes: a basic table which is a cross table by a single row variable and a single column variable; A grid table made up of one row by intersecting the table bases created by each row variable and column variable into a grid pattern by intersecting the table bases created by each row variable and column variable; Multi table that merges the table bases created by the internal variables of the multi-response variable according to the same value when the Row variable or the Column variable is the multi-response variable (MR or MD) into a single table; When both row and column variables are multi-response variables (MR or MD), the table bases created by intersecting the internal variables of the row variable and the internal variables of the column variable are merged by the row variable value and the column variable value. Multi-Multi table; Intersect table created by being nested by row or column variables, and the number of overlapping variables becomes the overlapping order, the first variable becomes the highest, and the last variable becomes the lowest, according to the order of the variables. Nested table made by weaving a table base created by AND conditionality by individual values of several variables in grid table form; Intersect table created by multiple multi-response variables (MR or MD), which is nested in the order of the internal variables of each multi-response variable, that is, the first multi-overlapping is the first variable of each multi-response variable. The nested multi- nested table means a nested final variable of each multi-response variable. Summary table, which is an aggregate table created by reintegrating tables and tables created from multiple table analysis tasks; T-Test comparing the difference of means for the mean, Cell percent, Top-N percent, and Bottom-N percent of the cross table by the Row and Column variables, Z-Test comparing the difference of ratios, and McNima A Testing table for outputting statistical comparison results such as McNemar-Test) and Chi-Square Test together in a table; It includes. In addition, Sorted table for sorting in ascending or descending order according to the value or frequency of the variable; Transposed table that converts rows and columns and displays them; A conditional table that creates a table only for response data corresponding to a specific condition; A Weighted table for creating a table based on weight variables; Split Table which creates a table separated by variable values; A String table that creates a table even when the response data is text as well as numeric; And the like.

The advanced statistical analysis step is to perform advanced statistical analysis such as basic statistical analysis, frequency analysis, cross analysis, difference comparison analysis, analysis of variance, correlation analysis, regression analysis, categorical data analysis, factor analysis, colony analysis, discriminant analysis, etc. . After the user creates the advanced statistical analysis task, sets the analysis variable, the analysis method and the statistics to be output, and executes the analysis, the customizing research server 100 descriptive for basic statistical analysis according to the analysis type and setting of each analysis task. Algorithm, Frequency algorithm for frequency analysis, Crosstabs algorithm for cross-analysis such as independence and association of row and column variables, Comparison comparison for difference analysis such as statistical verification of difference of average or statistical verification of difference of ratio Algorithm, ANOVA algorithm for analysis of variance to verify the effect of key factors in experiments or observations, Correlation algorithm for correlation analysis to see the relationship between variables, Regression algorithm for regression analysis, Categorical data analysis for categorical data analysis Data Analysis Algorithm, Factor Analysis for Factor Analysis Hierarchical Clustering Algorithm and K-Means Algorithm for colony analysis that classifies observations into a certain number of clusters, Discriminant Analysis Algorithm for discriminant analysis to determine which group the observations are extracted from, and Classification Algorithm for classifying observations The data analyzer engine implements a variety of advanced statistical analysis algorithms such as analysis of job data and response data, and saves the results generated for each analysis task in a certain form and outputs them to the user. .

A process of generating a report, which is the sixth process S600 of FIG. 3, will be described in detail with reference to FIG. 16 as follows.

As shown in FIG. 16, the report generation unit 124 may perform various operations according to options set by a user based on a standard format for a result obtained by executing a data processing and data analysis process, which is a fifth process (S500). Generates a report in the form of a report, or the user can generate all the research and result information such as the survey design, survey information, data information, analysis program information, analysis result information, and report information generated by each research step online. The research tool provides a research tool by reconstructing the research component including the procedures and contents of each research step according to the customer's needs or by providing a research tool for the customer to perform all procedures such as data processing, statistical analysis and report generation in real time. We provide customized research tools for you to do it yourself.

First, in the analysis report generation step, the report generator 134 generates, stores, and outputs an analysis report of various formats in the results of the table analysis and the advanced statistical analysis executed by the analysis unit 123 (S610).

At this time, it basically outputs the web document file format, and additionally stores and outputs the file in MS Excel file format, MS Word file format, MS PowerPoint file format, Korean file format, and the like.

Specifically, after analyzing the data in the analysis unit 123, the analysis result is stored in a certain form. Here, the user specifies the format in which the analysis results are saved or printed. Specify the table format such as the type of table provided in advance, the line and color of the table, and the font of the text.If there is a graph, the type of graph and the graph Specifies the format of the graph, such as layout, color, and font of text. Then, the user can specify the file format such as Web document file format, MS Excel file format, MS Word file format, MS PowerPoint file format, Korean file format, etc. to save or output the analysis results, and execute report creation. In addition, the customizing research server 100 generates a file having a format specified by the report generator engine according to the selected format and file format.

The web document file format report regenerates the analysis results in HTML format. The analysis results are saved by the web browser program because the analysis is saved as a separate HTML file for each analysis task and links to the results of each analysis task on the main page. You can easily open it.

Reports in the MS Excel file format regenerate analysis results in MS Excel format, outputting them in separate Excel sheets for each analysis task, printing the table of contents in the main sheet, and linking them to the sheets with output from each analysis task. Because of the connection, the user can easily open the analysis results by MS Excel program.

The report in MS Word file format regenerates the analysis results in MS Word format. It outputs each analysis task in one file, prints the contents of the analysis on the upper page, and links them to the location where the output of each analysis task is printed. Because of the connection, the user can easily open the analysis result by MS Word program.

Reports in the MS PowerPoint file format regenerate analysis results in MS PowerPoint format, outputting on a separate slide for each analysis task, outputting the table of contents on the main sheet, and linking to the slides with the output of each analysis task. Users can easily open the analysis results by using the MS PowerPoint program. At this time, if the user sets the graph option of the analysis task, the analysis task is output on the slide in the set graph format.

The report in Hangul file format regenerates the analysis results in Hangul format. It outputs each analysis task in one file, prints the contents of the analysis on the top page, and links them to the location where the results of each analysis task are printed. Therefore, the user can easily open the analysis result by the Hangul program.

Subsequently, the report generator 134 reports all the research performance and the result information such as the research design information, the survey information, the data information, the analysis program information, the analysis result information, the report information, etc. generated by the user in each research step to the customer. Generated as a file and provided online (S620).

The customizing research server 100 provides the generated web document files, office files such as MS Excel files, MS Word files, MS PowerPoint files, Korean files, etc. to customers online.

That is, the user transmits various types of data and report files to a folder of the server, and the customer can freely download the files after accessing the customized research server 100.

In addition, the report generator 134 determines the procedure and content to be performed at each research stage, reconfigures the research component according to the needs of the customer, and provides a research tool to allow the customer to access the customized research server 100 in real time. A customized research tool is provided to directly perform all procedures such as data processing, statistical analysis, and report generation (S630).

That is, the customizing research server 100 analyzes and analyzes data including the questionnaire form, data collection and processing information, job data, analysis script, analysis task, and reporting task, which performed data processing, data analysis, and reporting work according to the customer's needs. By providing all information such as analysis program to customer in a customized way, we provide service that customer can analyze data or generate report in real time.

As a result, customers are provided with direct and real-time services throughout the entire process, from survey forms to data collection and input, data analysis and reporting. This includes the way in which customers are provided with Internet-based research services online and the local-based research services as offline customized research system files.

Internet-based research service, when the user configures the research service to meet the needs of the customer and delivers to the customer of the customizing research server 100, the customer connects to the customizing research server 100 and the user receives the research service delivered to the customer Receive. As a result, customers can check all their research information, rerun the research process in real time, or reconfigure and run additional research services themselves.

Local-based research services allow users to configure research services to meet customer needs, create customized research system files, and deliver these files to customers, where the client runs local-based customized research programs to deliver the received files. Once loaded, the local-based custom research program will reconfigure the research service. Thus, the customer can directly perform the research service reconfigured in the local program without accessing the customizing research server 100. As a result, customers can check all their research information, rerun the research process in real time, or reconfigure and run additional research services themselves.

As described above and described with reference to a preferred embodiment for illustrating the technical idea of the present invention, the present invention is not limited to the configuration and operation as shown and described as described above, it is a deviation from the scope of the technical idea It will be understood by those skilled in the art that many modifications and variations can be made to the invention without departing from the scope of the invention. Accordingly, all such suitable changes and modifications and equivalents should be considered to be within the scope of the present invention.

1 is a block diagram conceptually illustrating a customizing research system according to the present invention;

2 is a detailed configuration diagram of a customizing research server according to the present invention.

3 is an overall flow chart of a custom research method according to the present invention.

4 is a detailed flowchart of a research configuration registration process (S100) according to the present invention.

5 is a detailed flowchart of a component property setting and an inter-component relationship setting process according to the present invention;

6 is a detailed flowchart of a survey design process (S200) according to the present invention.

7 is a detailed flowchart of a questionnaire preparation process (S300) according to the present invention.

8 is a detailed flowchart illustrating a process of receiving data after distribution of a questionnaire according to the present invention (S400).

9 is a flowchart of an error checking step S460 of response data according to the present invention;

10 is an overall flowchart of a data processing and data analysis process S500 according to the present invention.

11 is a flow chart related to the input data and variable information editing step (S510) according to the present invention.

12 is a flowchart of a step S520 of dividing long text response data into short text response data from data collected on-line and off-line in accordance with the present invention.

13 is a flowchart of a step S530 of classifying or grouping into similar groups according to the present invention.

14 is an exemplary view showing a code sheet according to the present invention.

15 is a flowchart of step S540 of performing data analysis in accordance with the present invention.

16 is a detailed flowchart of a report generating process S600 according to the present invention.

** Description of symbols for the main parts of the drawing **

S: Customizing Research System

100: customizing research server 200: payment server

300: user computer 110: database module

120: control module 111: member database unit

112: questionnaire database section 113: response database section

114: analysis database unit 115: report database unit

121: survey preparation unit 122: data management unit

123: analysis unit 124: report generation unit

125: automatic survey design

Claims (19)

In the custom research system, A database module for storing research member information, research questionnaire information, research response information, research analysis information, and research report information; and analyzing data received from the database module and controlling the database module according to the interpreted results. According to the procedure, the property of the component and the function of the relationship between the components are determined in accordance with the request signal of the user's computer, and according to the determined user research tool, the questionnaire is created and published online, offline, data collection, input of response data, A control module for executing data processing and data analysis and report generation process; Customizing research server comprising a; And A user computer accessing the customizing research server and transmitting and receiving data for receiving various user-oriented research services; Customizing research system comprising a. The method of claim 1, The database module, Customer member requesting research, researcher member conducting research, interviewer member of online and offline surveys, respondent member responding to survey, data editor member verifying and editing response data, data to input response data of offline survey A member database unit for storing input member members, personal information of researcher members who process and analyze response data, and research participation information; A survey form created for users to conduct research projects, a standard survey library that supports various types of standard surveys, a public survey library shared by users for use by multiple users, and a collection of frequently used surveys. Survey database unit consisting of a library; A response database unit configured to store collected data by respondents answering the questionnaire or inputters inputting response data; An analysis database unit for storing processing information and analysis information of the response data; And A report database unit for storing report preparation information; Customizing research system comprising a. The method of claim 1, The control module, A survey preparation unit which executes a process of preparing a survey required for an online survey and an offline survey; A data management unit that responds to a questionnaire through an online survey or an offline survey, collects data by inputting response data, or performs error checking on collected response data, and collects and inputs and manages the collected data; An analysis unit for analyzing the data by editing the response data, generating new data by adding and merging several data, reclassifying the response data, and executing table analysis and advanced statistical analysis; A report generation unit which executes a report generation process based on a result of statistically analyzing the response data; And An automatic survey design unit which executes the automatic survey design process according to the set condition; Customizing research system comprising a. In a method using a custom research system, (a) registering a research configuration by the custom research server by setting output forms, execution attributes, and execution procedures of each research component, and defining a connection structure, connection attributes, and execution directions between the research components; (b) executing, by the customization research server, the survey design by the basic information of the survey factors received from the user computer and the survey design method, and storing the created survey design result; (c) the customization research server creating a questionnaire (Quform) according to the survey design, defining a basic property and a view property of the questionnaire, and setting the relationship logic between the question logic and the questionnaire, thereby completing the questionnaire creation; (d) the customization research server receiving the collected data and checking for data errors; (e) analyzing the data by editing the input data by the customization research server, reclassifying the data, and then analyzing the data by performing a table analysis and an advanced statistical analysis according to an analysis procedure; And (f) the customization research server generating and servicing various types of reports or executing a real-time research tool; Customizing research method comprising a. The method of claim 4, wherein The (a) process, (a-1) the customization research server transmitting a research component list including member data, design data, questionnaire data, response data, external data, analysis data, and a report to the user computer 300; (a-2) receiving, from the user's computer, online and offline research work order determination information in which the research component is placed, and receiving research condition and environment setting information to be executed in each research step; And (a-3) generating a research tool configured by the customizing research server to set the properties of each component and the relationships between the components, to register the research, and to execute the research tool according to a user-designed research procedure; ; Customizing research method comprising a. The method of claim 5, In the step (a-3), the component property setting and the relationship setting, (a-3-1) setting, by the customization research server, an output form in a form of a view of a component execution result; (a-3-2) setting, by the customizing research server, an execution attribute which is an option for execution of a component; (a-3-3) setting, by the customizing research server, an execution procedure corresponding to a method of executing a component; (a-3-4) a step of establishing a connection structure in which the custom research server means a connection form between components; (a-3-5) a step of setting a connection attribute by the customization research server to determine a movement of a result between components; And (a-3-6) an execution direction setting step of determining, by the customization research server, an execution direction of components including unidirectional, bidirectional, and repetitive execution; Customizing research method comprising a. The method of claim 4, wherein Step (b) is, (b-1) receiving, by the customization research server, survey basic information including survey project title information, survey method information, survey subject information, survey field and item information, and survey type information from a user computer; (b-2) the customization research server receives information from any one of the stratified extraction method, the colony extraction method, the target allocation extraction method, the random extraction method, the systematic extraction method from the user computer, and the proportional distribution, Neiman distribution, Receiving any sample distribution method information of the significant distribution; (b-3) the customization research server executing a survey design; And (b-4) storing the survey design result of the customization research server; Customizing research method comprising a. The method of claim 4, wherein Step (c) is, (c-1) the customization research server transmitting a menu screen to select a questionnaire of any form among a new questionnaire form, an existing questionnaire form, an external questionnaire file, and a questionnaire library to a user computer; (c-2) the customization research server determining whether the question form selection signal received from the user computer is a new question form opening selection signal; (c-3) in the case where the determination result of step (c-2) is a signal for selecting a new questionnaire form, the customization research server generating and storing a new questionnaire form and transmitting the new questionnaire form to the user computer; (c-4) generating, by the customization research server, working data; (c-5) the customization research server receiving and storing the questionnaire information which is a basic attribute from the user computer; (c-6) setting a questionnaire context by receiving, from the user's computer, logic for the relationship between the view property and the question logic and the questionnaire; (c-7) generating, by the customization research server, an automatic variable; And (c-8) the customization research server creating a questionnaire form preview and transmitting the preview form to the user computer, and storing the questionnaire form; Customizing research method comprising a. The method of claim 8, (c-9) if the determination result of the step (c-2) is not a new questionnaire form selection signal, determining whether the customizing research server is an existing questionnaire form selection signal; (c-10) when the determination result of step (c-9) indicates that the existing questionnaire form selection signal is received, the customizing research server receives an existing questionnaire form list and transmits the list to the user computer; And (c-11) the customization research server receives the selected existing questionnaire form from the user computer, generates and stores the received existing questionnaire form as a new questionnaire form, and then transmits it to the user computer (c-4). Implementing the procedure to step); Customizing research method characterized in that it further comprises. The method of claim 9, (c-12) determining that the customization research server selects an external questionnaire file selection signal when the determination result of step (c-9) is not an existing questionnaire open selection signal; (c-13) When the external survey file selection signal is determined as the result of the step (c-12), the customizing research server receives the external questionnaire file in Word and Excel format from the user computer, and selects the external questionnaire file from the external questionnaire file. Loading survey information; And (c-14) The customization research server analyzes the section information, classifies it into a questionnaire type, questionnaire content, view item, and attribute item, generates a questionnaire information, converts it to a questionnaire form, stores it, and transmits it to the user's computer. Implementing the procedure in step (c-4); Customizing research method characterized in that it further comprises. 10th According to claim, (c-15) determining that the customization research server is a questionnaire open selection signal when the external survey file selection signal is not determined as a result of the determination in step (c-12); And (c-16) As a result of the determination in step (c-15), when the open questionnaire library is a selection signal, the customizing research server receives the questionnaire library, converts the questionnaire form into a questionnaire form, generates a new questionnaire form, and stores it. Transmitting to the user computer and implementing the procedure to step (c-4); Customizing research method characterized in that it further comprises. The method of claim 4, wherein (D) process, (d-1) receiving, by the customization research server, data from the user computer 300 in response to a web questionnaire posted on the web online; (d-2) receiving, by the customization research server, data from the user computer 300 in response to the web questionnaire distributed by e-mail; (d-3) receiving data from the interviewer from the interviewer computer in which the customized research server is a mobile notebook through an interview with the interviewer and the respondent; (d-4) receiving, by the telephone interviewer, data input by the respondent directly into the web questionnaire distributed for the telephone survey, so that the customization research server receives the response data through the telephone interview computer; And (d-5) The customized research server inputs the response data in full form by inputting the data responded by the respondent directly into a paper questionnaire distributed directly to the respondent by the interviewer for offline research. Receiving an input from a computer; And (d-6) checking, by the customization research server, an error of the input response data; Customizing research method comprising the 13. The method of claim 12, The step (d-6), (d-6-1) The customizing research server has its own attributes for each question type based on a random question form, a question logic, and variable information and response data of job data connected to the question form. Checking logic according to; (d-6-2) The customization research server includes a non-response check of a question between a specific questionnaire and a moving target questionnaire by a questionnaire, an acceptance value check, or a reject value check between questionnaires. Examining the questionnaire extended logic; And (d-6-3) the customization research server transmitting an error check result to the user computer, and receiving and storing the modified response data according to the response data in error from the user computer; Customizing research method comprising a. The method of claim 4, wherein The above (e) process, (e-1) editing, by the customization research server, input data and variable information; (e-2) dividing the long text response data into short sentences or classifying other text response data from the input data by the custom research server; (e-3) classifying the text or numerical data into a similar group and classifying or regrouping the text or numerical data in the input data by the customization research server; And (e-4) the custom research server executing data analysis; Customizing research method comprising a. The method of claim 14, The step (e-1), (e-1-1) transmitting the variable information to the user computer by the custom research server; (e-1-2) The customization research server receives variable information of variable name, variable label, variable value and variable value label from the user computer, and changes and stores the variable information of the job data. ; (e-1-3) The custom research server transmits response data corresponding to all variables for all respondents to the user computer in the form of a grid sheet, and receives the changed response value from the user computer 300. Applying to response data; (e-1-4) editing, by the customization research server, using the command syntax; And (e-1-5) The custom research server merges several response data by adding all or part of response data and variables of different job data to a column, or adds them as a case to generate a new response data. Making; Customizing research method comprising a. The method of claim 14, Step (e-2), (e-2-1) transmitting, by the customization research server, text data and questionnaire of the respondent with the content of the long questionnaire to the user's computer; (e-2-2) the customization research server receiving and storing a content indicating a location where the long text is divided from a user computer; (e-2-3) generating, by the custom research server, new variables as long as the long text is divided, and storing the divided texts in columns of response data of the newly created variables; And (e-2-4) separating and storing the long response data of all respondents separately divided by the user computer into columns of the response data of the newly created variable by the custom research server; Customizing research method comprising a. The method of claim 14, The step (e-3), (e-3-1) the custom research server transmitting a text data list to the user computer 300 based on a recording task of working data, variables of the working data and text data; (e-3-2) The custom research server receives a selection signal according to transmission of a text data list of any one of a new text recording selection signal, an external codebook file recording selection signal, and an open bank coding selection signal from the user computer 300. Generating a new codebook and configuring a code set accordingly; (e-3-3) Representative codes and subcodes of codebooks generated by receiving or directly loading information classified as subcodes by the customization research server by selecting a predetermined list of texts and adding them to the representative codes. Automatically classifying according to; And (e-3-4) the customization research server classifies all text lists according to the codebook, and then executes a recording process to encode and convert all the texts of the response data according to the classification scheme of the codebook and to store the response data. ; Customizing research method comprising a. The method of claim 14, The step (e-4) is, (e-4-1) creating, by the customizing research server, a table analysis task in various tabular forms with respect to the input data; And (e-4-2) Advanced Customizing Analysis Server includes customized analysis server including frequency analysis, cross analysis, difference comparison analysis, analysis of variance, correlation analysis, regression analysis, categorical data analysis, factor analysis, discriminant analysis, and colony analysis Creating a task; And (e-4-2) the custom research server executing an analysis; Customizing research method comprising a. The method of claim 4, wherein The above (f) process, (f-1) generating analysis reports in various formats from the results of the table analysis and the advanced statistical analysis executed by the customization research server; (f-2) the customization research server generating all the research performance and result information as a report data file and providing the data online; And (f-3) providing a customized research tool so that the customized research server can directly perform all procedures including data processing, statistical analysis and report generation in real time; Customizing research method comprising a

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