Questionnaire system and method

Abstract

The invention discloses a questionnaire system and method, and relates to the technical field of information processing, and the questionnaire system comprises a questionnaire design module, a questionnaire publishing module, a data collection module and a data analysis module. Wherein the data analysis module adopts a mode of combining descriptive statistical analysis with deep analysis such as correlation analysis, difference test, multivariate statistical analysis and text analysis to realize deep and comprehensive data analysis; the questionnaire method comprises the steps of questionnaire design, questionnaire release, data collection and data analysis, the questionnaire system and method can improve the efficiency and flexibility of questionnaire design, improve the accuracy and efficiency of data collection, guarantee the safety of system use and data privacy, remarkably improve the efficiency and quality of questionnaire work, and reduce the labor intensity of workers. And powerful support is provided for data collection and analysis in various fields.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a survey questionnaire system and method capable of efficiently designing, publishing, collecting, and analyzing questionnaires. Background Technology

[0002] In today's digital age, questionnaires are widely used in market research, academic research, social surveys, and many other fields to obtain valuable data. However, existing questionnaire methods have many drawbacks. Traditional paper questionnaires not only consume a lot of manpower, resources, and time for printing, distribution, and collection, but also involve cumbersome data entry and statistical analysis, are prone to human error, and have extremely low data processing efficiency. While electronic questionnaires have improved data collection efficiency to some extent, they generally suffer from limited questionnaire design functions, making it difficult to meet complex logical requirements. For example, designing questionnaires with complex jump relationships or related questions is difficult to operate and lacks flexibility, making it hard to meet the needs without programming or scripting. At the same time, the data analysis functions of existing electronic questionnaire systems are relatively simple, mostly only capable of simple data aggregation, unable to deeply explore the potential relationships and trends behind the data, and failing to meet the increasingly diverse and refined data processing needs of users. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing questionnaire methods and provide a comprehensive, user-friendly, and data-analyzing questionnaire system and method to significantly improve the efficiency and quality of questionnaire work and provide strong support for data collection and analysis in various fields.

[0004] To solve the above-mentioned technical problems, one of the technical solutions adopted by the present invention is to provide a questionnaire system, comprising: Questionnaire Design Module: Provides an intuitive and visual questionnaire editing interface. Users do not need professional programming knowledge to freely add, delete, and modify various question types through simple drag-and-drop and click operations. It supports detailed settings for question type attributes, such as setting required fields to ensure the collection of key information. It also allows for randomization of option order to avoid interference with user selection and uses natural language to complete complex logic settings. Questionnaire distribution module: Generates a unique link or QR code for each completed questionnaire, allowing users to easily distribute it through various channels. Distribution channels include SMS (including 5G SMS), email, social media platforms (such as WeChat, Weibo, Douyin, etc.), and website embedding. It also integrates third-party data to maximize reach to the target survey population and achieve precise targeting. It supports setting rich distribution parameters, such as the questionnaire's validity period, which can be specified down to a specific date and time period, allowing flexible control over the questionnaire collection time. It also allows setting limits on the number of times a questionnaire can be completed to prevent the same user from submitting multiple responses, ensuring the accuracy and validity of the data.

[0005] Data collection module: Used to collect questionnaire data filled in by users in real time. It has powerful data validation functions. When users submit data, the system automatically performs format validation. For example, for numeric questions, it checks whether the input is a valid number; for date questions, it verifies whether the date format is correct. At the same time, it performs a completeness check to ensure that all required fields are filled in. For data that does not meet the requirements, it promptly provides clear prompts to users to guide them to make corrections, ensuring that the collected data is accurate.

[0006] The data analysis module is used to perform descriptive and multi-dimensional analysis on the collected data, and generate data visualization charts such as bar charts, line charts, pie charts, and radar charts to present data characteristics in a more vivid and easy-to-understand way, allowing users to quickly grasp the key points of the data without complicated data interpretation. At the same time, it supports data filtering and in-depth analysis based on specific conditions set by the user to uncover the potential patterns, correlations, and rules hidden behind the data.

[0007] The user management module is responsible for the comprehensive management of users of this survey system. It provides user registration and login functions, ensuring the security of user accounts. Simultaneously, it features a flexible access control mechanism, with different users having different operational permissions. For example, administrators have the highest privileges, allowing them to perform system settings, view all survey data, and manage user information. Regular users can only create, edit, and publish their own surveys and view survey data related to themselves, effectively protecting data privacy and security. Built-in collaborative filtering algorithms can intelligently establish connections between surveys and target respondents, facilitating precise selection and targeted delivery.

[0008] Preferably, the question types described in the questionnaire design module include, but are not limited to, single-choice questions, multiple-choice questions, numerical questions, open-ended questions, scale questions, grid questions, matrix questions, and audio / video questions, etc., which are rich and diverse and fully meet the needs of different survey scenarios.

[0009] Preferably, the questionnaire design module can also create complex questionnaire logic, such as redirection logic, where the system automatically redirects to a preset page of related questions after a user selects a specific answer to a question, achieving a personalized questionnaire process; and association logic, which links answers to different questions to gain a deeper understanding of users' thinking patterns and behavioral characteristics. The assignment operation automatically assigns values ​​to questions where respondents have explicitly or implicitly indicated their answers. The sequence control function allows setting the order of the questionnaire globally or partially as needed, such as natural order, cyclical order, or random order. The extraction function allows extracting portions of the questionnaire according to certain rules for respondents to answer.

[0010] Preferably, the descriptive analysis in the questionnaire analysis module quickly calculates basic statistics such as mean, median, mode, frequency distribution, kurtosis, skewness, and percentiles, intuitively presenting the central tendency and dispersion of the data. The multidimensional analysis includes correlation analysis, difference testing, text analysis, and multivariate statistical analysis. Correlation analysis can delve into the relationships between different variables, such as analyzing the correlation between user age and satisfaction with a product. Difference testing compares whether the differences between different groups of data are statistically significant, helping users discover significant differences in survey questions among different groups. Frequency analysis algorithms extract high-frequency keywords, enabling in-depth analysis of feedback from short-answer questions, further clarifying hot topics and main directions, providing more targeted guidance for future improvements. Multivariate statistical analysis includes multivariate regression analysis, factor analysis, cluster analysis, discriminant analysis, and multidimensional scaling, uncovering the underlying patterns in the data at a higher level.

[0011] To solve the above-mentioned technical problems, the second technical solution adopted by the present invention is to provide a questionnaire survey method, which uses the above-mentioned questionnaire survey system and includes the following steps: S1. Questionnaire Design: After logging into the survey system, users enter the questionnaire design module. In the visual editing interface, they add the required question types according to the survey purpose and needs, and set the question type attributes in detail. Using the logic setting function provided by the system, they create the internal jump logic, association logic, answer order, and question selection rules of the questionnaire to build a complete and reasonable questionnaire structure. After completing the addition and setting of all questions, users can preview the questionnaire through the system's preview function to check whether the page layout, question wording, and logical relationships are correct and reasonable. If necessary, multiple modifications and tests can be performed to ensure that the questionnaire reaches its best state before official release. S2. Questionnaire Distribution: After confirming that the questionnaire design is correct, users can use the questionnaire distribution module. The system will generate a unique link or QR code for the questionnaire based on the user's selection. Users can choose appropriate distribution channels based on the characteristics of the target audience, such as sending SMS (including 5G SMS) or email to the target customer group, publishing questionnaire information on social media platforms, or embedding questionnaire links on relevant website pages, to accurately push the questionnaire to the target audience. During the distribution process, users can set distribution parameters such as the validity period of the questionnaire and the limit on the number of times it can be filled out, to ensure the smooth progress of the questionnaire collection and the validity of the data. S3. Data Collection: After receiving the questionnaire, the target audience enters the questionnaire page by clicking the questionnaire link or scanning the QR code. Guided by the questionnaire page, users fill in their personal information, answer various survey questions, and submit their answers. The system collects the data submitted by users in real time. At the moment of data submission, the system automatically starts the format validation, logic check, and integrity check procedures. For data that does not meet the format requirements or is missing required fields, the system immediately pops up a prompt window to inform the user of the error information and modification requirements until the user corrects the data and successfully submits it, ensuring the accuracy and integrity of the data. S4. Data Analysis: After collecting a certain amount of data, users enter the data analysis module. First, they select the data range to be analyzed based on the analysis objective and determine the analysis method, such as descriptive statistical analysis, correlation analysis, difference test, multivariate statistical analysis, etc. The system automatically calculates and analyzes the data according to the user's selection and quickly generates corresponding data visualization charts and detailed analysis reports. Users can further adjust the analysis parameters based on the analysis results, such as changing data filtering conditions or changing analysis methods, to deeply explore data information and provide strong data support for decision-making.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. Improve questionnaire design efficiency and flexibility: The visual questionnaire design interface, along with rich question types and natural language-based logic settings, enables users to easily create questionnaires with complex logic, greatly shortening the questionnaire design cycle, improving work efficiency, and meeting diverse survey needs.

[0013] 2. Improve the accuracy and efficiency of data collection: Real-time data verification effectively avoids the collection of erroneous data and ensures data quality; multiple distribution channels and convenient filling methods improve the questionnaire return rate and make data collection more efficient.

[0014] 3. Enable in-depth and comprehensive data analysis: Various descriptive statistics, multi-dimensional data analysis functions, and intuitive data visualization charts help users gain a deeper understanding of data characteristics and potential correlations, providing scientific and accurate data basis for decision-making in various fields.

[0015] 4. Ensure system security and data privacy: A robust user management and permission setting mechanism ensures the secure operation of the system, protects user data privacy, and prevents data leakage and misuse. Attached Figure Description

[0016] Figure 1 This is a block diagram of the questionnaire system of the present invention.

[0017] Figure 2 This is a flowchart of the questionnaire method of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] This embodiment provides a questionnaire system, such as Figure 1 As shown, it includes a questionnaire design module, a questionnaire distribution module, a data collection module, and a data analysis module.

[0020] The questionnaire system of this invention can be deployed on a high-performance server. The server needs to have sufficient memory (8GB or more), large storage space (not less than 500 GB), and strong processing power (such as a 16-core CPU) to ensure that the system can run stably and efficiently. User terminals can be common devices such as personal computers, tablets, and smartphones, which connect to the server via the Internet to use this system.

[0021] The server-side operating system will be a mainstream Linux (such as CentOS or Ubuntu) or Windows Server system. A powerful relational database management system, such as MySQL or Oracle, will be used to store important data such as questionnaire data and user information. The system development language can be Java or Python, utilizing relevant development frameworks and tools, such as Spring Boot and Django, for system development and construction. User terminals will access the system through common browsers (such as Chrome, Firefox, Edge, and Safari), and the system should ensure compatibility and a good user experience across different browsers.

[0022] The questionnaire process is as follows: Figure 2 As shown, the following steps are included: S1. Questionnaire Design: Users open their browser, enter the questionnaire system's URL in the address bar, and access the login page. After successfully logging in with their registered account and password, they click to enter the questionnaire design module. In the visual editing page, click the "Add Question" button, select the desired question type from the drop-down menu (e.g., multiple choice), and in the pop-up settings window, enter the question content, set options, and check required fields. To set jump logic, click the logic settings button, select an option for the current question, and set the jump target question. The order of answering questions (natural, cyclical, or random) can be set globally or locally. For questions where answers are implicit in previous questions, automatic assignment can be set. For excessively long questionnaires, rules can be set to select a portion of questions for each respondent to answer. During the question setting process, users can click the "Preview Questionnaire" button at any time to view the questionnaire's effect. If there are any problems, they can return to the editing page to modify it until the questionnaire design is satisfactory. S2. Questionnaire Publication: On the questionnaire design page, click the "Publish Questionnaire" button. The system will pop up a publishing settings window. Users can choose to generate a link or a QR code, and can choose whether to generate both simultaneously as needed. In the publishing channel options, select the channels to be published, such as email. The system will pop up an email editing window. Users can import the target email address, email subject and content (which may include the questionnaire link or QR code), and click send email. If choosing to publish on social media platforms, users can copy the questionnaire link or QR code, log in to the corresponding social media account, paste the link or insert the QR code in the published content, edit the introductory text, and click publish. At the same time, set the validity period and the limit on the number of times the questionnaire can be filled, and click the "Confirm Publish" button. S3. Data Collection: After receiving the questionnaire, the target audience opens the link in their browser or scans the QR code using a mobile phone to enter the questionnaire page. Following the on-screen instructions, they fill in their personal information, such as age and gender; answer various questions, such as clicking the option for multiple-choice questions and entering numbers for number questions. After completing the questionnaire, they click the "Submit Questionnaire" button. The system verifies the submitted data. If the data format is incorrect or required fields are missing, a prompt window will pop up. Users can then modify the data according to the prompts and resubmit until the data verification is successful and the system saves the data. S4. Data Analysis: After logging into the system, users enter the data analysis module. In the data selection area, they select the questionnaire data to be analyzed. In the analysis method selection box, they choose descriptive statistical analysis and set the variables to be analyzed, such as course satisfaction and user age. Clicking the "Start Analysis" button, the system quickly calculates the data and generates visual charts such as bar charts and line charts, displaying detailed statistical data such as mean and median. If users want to further analyze correlations, they switch the analysis method to correlation analysis, select the two variables whose correlation they want to analyze, and click the "Analyze" button. The system generates the correlation analysis results, which users can then use to further adjust their analysis strategies and delve deeper into the value of the data.

[0023] Trial Case: Evaluation of Corporate Employee Training Effectiveness Case Background: To improve employee skills, a large enterprise regularly organizes various training courses, including new employee onboarding training, professional skills enhancement training, and management skills training. To understand the training effectiveness and optimize subsequent training programs, the company decided to use the questionnaire system described in this example to evaluate the training effectiveness for its employees.

[0024] Implementation steps: 1. Questionnaire Design: The company's human resources department and training team jointly designed the questionnaire. After logging into the survey system, they added single-choice questions to gather basic information about employees, such as their department, job level, and the number of training sessions they attended. Multiple-choice questions were added, allowing employees to select the types of training courses they had attended. Rating scale questions were added, allowing employees to evaluate the practicality of the training content, the instructor's teaching ability, and the training organization and arrangement on a scale of 1-5. Open-ended questions were included to solicit employees' suggestions for improving the training content and their preferred training topics.

[0025] In terms of questionnaire logic, a jump logic is used. If an employee selects that they have not participated in a certain type of training, the system skips the detailed evaluation questions for that type of training. Through association logic, the system links the employee's evaluation of the training course with specific training feedback questions. For example, if an employee gives a low rating to a professional skills training course, the system automatically asks, "In what aspects do you think this professional skills training needs improvement?"

[0026] 2. Questionnaire Distribution: The questionnaire distribution module generates questionnaire links and QR codes. The company publishes a questionnaire notification on its internal office system homepage, along with the questionnaire link, requiring employees to complete it within one week. Simultaneously, the questionnaire QR code and reminder messages are shared in internal instant messaging groups such as WeChat and QQ groups, allowing employees to quickly access the questionnaire page. To ensure comprehensive questionnaire completion, a questionnaire completion status tracking function is implemented; for employees who have not completed the questionnaire, the human resources department reminds them via email or phone.

[0027] 3. Data Collection: After receiving the questionnaire notification, employees access the questionnaire page and complete it within the specified time. The system verifies the data in real time. For data that does not meet the requirements, such as entering incorrect characters in the department selection field, the system will prompt the employee to fill in the questionnaire again. For required fields that are not filled in, employees will be clearly prompted to complete them to ensure data quality.

[0028] 4. Data Analysis: Descriptive statistical analysis: Utilize the data analysis module to conduct detailed descriptive statistical analysis, accurately determining the training participation of employees in different departments and job levels, as well as the participation rate of various training courses.

[0029] Calculate the percentage of employees in different departments, assuming each department is denoted as . ,department The number of employees is The total number of employees is Then the department Employee ratio .

[0030] Calculate the training course participation rate, assuming the training course is... Attend training courses The number of employees is Total employees Then the course participation rate .

[0031] For each indicator in the scale, such as the practicality of the training content (set as a variable) ), Training instructor's teaching ability (set as a variable) (e.g., using formulas to accurately calculate the mean and median. Taking the practicality of the training content as an example, it includes...) Each employee rated the practicality of the training content, with scores as follows: average score The median is calculated by sorting all scores in ascending order. If it is an odd number, the median is the th. A fraction; if If the number is even, the median is the th. Individual and The average of the scores. By comparing the average and median scores, we can gain a comprehensive understanding of employees' overall evaluation of various aspects of the training. The calculated average score for the practicality of the training content was 4.2, which is... The average score for the training instructors' teaching ability was 4.6, which means... .

[0032] Correlation analysis: Pearson correlation coefficient was used to investigate the relationship between employee tenure and training satisfaction. Let tenure be λ. Training satisfaction rate: Pearson correlation coefficient ,in This represents the average number of years employed. This represents the average training satisfaction rate. The sample size is given. Through rigorous calculations, it was found that the Pearson correlation coefficient between senior managers' satisfaction with management skills training and the training effectiveness reached 0.823, indicating a high correlation between the two.

[0033] Difference testing: Analysis of variance (ANOVA) was used to compare the differences in training satisfaction among employees in different departments. Let the departments be... Training satisfaction rate: Suppose there is There are [number] departments, and each department has [number] departments. Sample Total number of samples Calculate the sum of squares between groups. Sum of squares within a group , The statistic is By comparison By identifying key values ​​and critical thresholds, we can pinpoint departments with good training results and those that need improvement.

[0034] Text Analysis: Deep text analysis is performed on employee suggestions in short-answer questions, and high-frequency keywords are extracted using a word frequency statistics algorithm. The text input by employees is segmented into words, and the resulting vocabulary set is set as follows: vocabulary The number of times it appears is The total number of words is Then vocabulary word frequency The study identified key issues raised by employees, such as "the training content is not closely related to actual work" and "the training schedule is unreasonable." Simultaneously, topic modeling algorithms, such as the Latent Dirichlet Allocation (LDA) model, were used to mine themes from employee feedback texts, further clarifying the main directions and hot issues raised, providing more targeted guidance for training improvement.

[0035] 5. Application of results: Training content optimization: Based on employee feedback, to address the issue of training content not being closely aligned with actual work, the training team invited key personnel from business departments to participate in the training content design, ensuring that the training content is more relevant to actual work needs. Regarding the employee request to add a training topic, "Digital Office Skills Enhancement," the training plan was promptly adjusted, and related courses were added.

[0036] The above description is merely the preferred embodiment of the present invention. It should be noted that, for those skilled in the art, various modifications or equivalent substitutions can be made to the technical solution of the present invention without departing from the principle of the present invention, and these modifications or substitutions can also achieve the technical effects of the present invention, and should also be considered to fall within the protection scope of the present invention.

Claims

1. A questionnaire survey system, characterized in that, include: The questionnaire design module provides a visual editing interface, allowing users to freely add, delete, and modify various question types, set question type attributes, and create complex questionnaire logic using natural language. The questionnaire publishing module is used to generate a unique link or QR code for the designed questionnaire, which can be published through multiple channels and the target respondent tags, questionnaire validity period and number of times limit can be set. The data collection module is used to collect questionnaire data filled in by users in real time and to perform format verification and integrity checks on the data. The data analysis module is used to perform descriptive and multi-dimensional analysis on the collected data, generate data visualization charts, and support data filtering and in-depth analysis based on user-defined conditions. The user management module is used to register, manage logins, and set permissions for users of the system. It also uses collaborative filtering algorithms to accurately target users and select target respondents.

2. The questionnaire system according to claim 1, characterized in that: The question types described in the questionnaire design module include single-choice questions, multiple-choice questions, numerical questions, open-ended questions, scale questions, grid questions, and / or matrix questions.

3. The questionnaire system according to claim 1, characterized in that: The complex questionnaire logic described in the questionnaire design module includes jump logic, association logic, assignment operation, answer order control, and question or option extraction operation.

4. The questionnaire system according to claim 1, characterized in that: The in-depth analysis includes descriptive statistics, correlation analysis, difference tests, multivariate statistical analysis, and text analysis.

5. A questionnaire survey method, characterized in that, Using the system according to any one of claims 1-3 includes the following steps: S1. Questionnaire Design: After logging into the system, users can add the required question types, set question type attributes and questionnaire logic in the visual editing interface, and then preview and test the questionnaire. S2. Questionnaire Distribution: Generate a questionnaire link or QR code, push it to the target audience through online or offline channels, and set the questionnaire validity period and the number of times it can be filled out; S3. Data Collection: The target audience completes a questionnaire, and the system collects and verifies the data in real time. S4. Data Analysis: Select the data range and analysis method, and the system will automatically calculate and generate visual charts or analysis reports. Users can adjust parameters for in-depth analysis.

6. The questionnaire survey method according to claim 5, characterized in that: In step S1, the user can freely choose single-choice questions, multiple-choice questions, numerical questions, open-ended questions, scale questions, network questions, and / or matrix questions.

7. The questionnaire method according to claim 5, characterized in that: In step S3, the system promptly provides feedback to the user regarding data that does not meet the format requirements or for which required fields are not filled.

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