Survey response data processing device, survey response data processing method, and survey response data processing program

Abstract

To provide a questionnaire answer data processing device, etc. that can effectively exclude low-quality answers that should be excluded when tabulating answers of a questionnaire survey and subsequently analyzing the answers.SOLUTION: A questionnaire answer data processing method includes the steps of: acquiring a plurality of pieces of answer data including content data indicating a content of answers to a questionnaire conducted over a network in the form of being used to answer a question, and identification data for identifying the answers (step S1); and extracting and deleting the answer data corresponding to low-quality answers that meet a default low-quality standard for answers from among the acquired answer data (steps S2 to S4). At this time, the low-quality standard is configured to be any or all of a standard for discriminating contradictory answers, a standard for discriminating low response answers, and a standard for discriminating distributional questionable answers.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to the technical fields of a questionnaire response data processing device, a questionnaire response data processing method, and a program for processing questionnaire response data, and more specifically to the technical fields of a questionnaire response data processing device, a questionnaire response data processing method, and a program for the questionnaire response data processing device that processes response data indicating responses to a questionnaire conducted over a network such as the Internet in the form of requesting answers to questions. [Background technology]

[0002] Conventionally, so-called questionnaire surveys have been commonly conducted for the purpose of, for example, market research, etc. The methods used for such surveys include, for example, mailing questionnaires to randomly selected homes or visiting the homes to request responses to the questionnaire questions, randomly selecting passersby on the street to respond, or calling randomly selected homes to obtain responses to questions.

[0003] In contrast, in recent years, with the widespread use of so-called smartphones, surveys are increasingly being conducted by sending data containing questions over the Internet to the smartphones of those expected to respond, and then obtaining the responses again over the Internet.Such surveys using smartphones and the Internet have the advantage of being able to collect responses quickly and inexpensively.

[0004] On the other hand, the above-mentioned method of conducting questionnaire surveys using smartphones and the Internet has problems such as the fact that the screens of smartphones themselves are small and respondents often input answers while on the move, so if there are a large number of questions or they cover a wide range of topics, the credibility of the answers decreases (i.e., only "careless answers" are obtained). More specifically, the following problems (a) to (c) actually exist.

[0005] (a) There are a certain number of respondents who give contradictory answers to different questions, for example, respondents who answer that they have "visited" a certain place (such as a tourist spot) but at the same time answer that they "do not know" that place (hereinafter, such respondents will be referred to simply as "contradictory respondents").

[0006] (i) For example, there are a certain number of respondents who, even when asked a question that includes dozens of beverages, answer "I have never drunk any of them," that is, respondents who are expected to find it troublesome to read all the options (hereinafter, such respondents will be referred to simply as "low responders").

[0007] (c) For example, in a questionnaire survey about a product that is generally considered to have almost no teenage customers, if the number of teenage respondents who answered "I use it" is excessively high compared to respondents of other age groups, that is, there may be doubts about the statistical distribution of the teenage respondents, and it may be difficult to treat them as legitimate respondents. Such respondents are likely to give false answers because they want to receive compensation for their answers.

[0008] Furthermore, if the survey results were compiled by treating respondents such as those described in paragraphs (A) to (C) above in the same way as respondents who provided other valid responses, the statistical distribution of the survey results would be adversely affected, and there would be a possibility that the intention of the plan or decision-making corresponding to the survey would be incorrect. Therefore, it is necessary to exclude the responses of respondents such as those described in paragraphs (A) to (C) above from the compilation of survey results. For example, Patent Document 1 listed below is an example of a document disclosing prior art that has been considered with the aim of meeting such a request.

[0009] The technology described in Patent Document 1 measures the time required for respondents to answer questions, calculates a representative value for the answer time required by respondents for each question, calculates a response time index for each respondent for the questions answered, and calculates an average value by dividing the sum of the response time indexes by the number of questions answered.The survey is then compiled by excluding a predetermined percentage of responses with the smallest average value. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Patent No. 4795496 Summary of the Invention [Problem to be solved by the invention]

[0011] However, respondents such as those described in paragraphs (a) to (c) above are thought to be so-called "accustomed to questionnaire surveys," and so may intentionally manipulate their responses to make the response time appropriate, as described in Patent Document 1, or may manipulate their responses so as not to appear to be inappropriate by diversifying the trends in their responses.As a result of these, there is a problem in that respondents such as those described in paragraphs (a) to (c) above may appear to be legitimate respondents.

[0012] Furthermore, it is possible for a survey implementer (e.g., a research company conducting the survey) that obtains the response data itself as a result of the survey to exclude response data obtained from respondents as described in paragraphs (A) to (C) above. However, it is difficult for an analyst (e.g., a survey analysis company conducting the analysis) that analyzes the response data obtained from the survey implementer to distinguish response data obtained as a result of the above-mentioned inappropriate response manipulation from other legitimate (i.e., highly reliable) response data. Therefore, there is a problem in that the analyst is forced to analyze the entire response data, including the response data resulting from the response manipulation (in other words, an inaccurate analysis).

[0013] Furthermore, when considering using AI (Artificial Intelligence) to increase the number of response data (expand the response data) as a result of a questionnaire survey, such as in the data generation method by the inventors of the present invention described in JP 2021-179865 A, if the response data obtained from respondents as described in paragraphs (A) to (C) above is included in the original response data, the AI ​​will expand the response data based on the included response data. As a result, there is also the problem of a decrease in the quality of the expanded response data.

[0014] The present invention has been made in consideration of the above-mentioned problems, and one example of its objective is to provide a questionnaire response data processing device, a questionnaire response data processing method, and a program for such a questionnaire response data processing device that can effectively exclude low-quality responses that should be excluded when compiling questionnaire survey responses and when subsequently analyzing the responses. [Means for solving the problem]

[0015] In order to solve the above problem, the invention described in claim 1 comprises: an answer data acquisition means such as a processing unit that acquires a plurality of answer data including content data indicating the content of the answers to a questionnaire conducted over a network in the form of requesting answers to questions and identification data for identifying the answers; an extraction means such as an inconsistent answer cleaning unit that extracts, from the acquired answer data, low-quality answer data corresponding to low-quality answers that are answers that fall under a low-quality standard that is preset as the answer; and a deletion means such as an inconsistent answer cleaning unit that deletes the low-quality answer data from the acquired answer data based on the identification data indicating the extracted low-quality answer data. a generating unit such as a generating unit that generates new complementary answer data in a number corresponding to the number of deleted low-quality answer data, based on the answer data after the low-quality answer data has been deleted, to replace the deleted low-quality answer data; The low-quality criteria are configured to be any one or all of: (i) a first low-quality criterion: when the answers to the multiple questions are mutually contradictory, each answer is designated as a low-quality answer; (ii) a second low-quality criterion: when the number of selections in the answer to the multiple-choice question is less than a predetermined number, the answer is designated as a low-quality answer; and (iii) a third low-quality criterion: when the answer by an answerer belonging to a different answer group from the answerer group from which the answer is expected, the answer is designated as a low-quality answer.

[0016] In order to solve the above problem, claims 6 The invention described in the item (1) includes a response data acquisition means, an extraction means, and a deletion means, generating means; an answer data processing method executed in an answer data processing device comprising: an answer data acquisition step of acquiring, by the answer data acquisition means, a plurality of answer data each including content data indicating the content of an answer to a questionnaire conducted over a network in a format of requesting an answer to a question and identification data for identifying the answer; an extraction step of extracting, by the extraction means, low-quality answer data corresponding to a low-quality answer that is an answer that falls under a low-quality standard that is preset as the answer from the acquired answer data; and a deletion step of deleting, by the deletion means, the low-quality answer data from the acquired answer data based on the identification data indicating the extracted low-quality answer data. a generating step of generating, by the generating means, new complementary answer data in a number corresponding to the number of deleted low-quality answer data, based on the answer data after the low-quality answer data has been deleted, to replace the deleted low-quality answer data;The low quality criteria are configured to be any or all of: (i) a first low quality criterion: if the answers to a plurality of the questions are mutually contradictory, each answer is designated as a low quality answer; (ii) a second low quality criterion: if the number of selections in the answer to the multiple-choice question is less than a predetermined number, the answer is designated as a low quality answer; and (iii) a third low quality criterion: if the answer by an answerer belonging to a different answer group from the answerer group from which the answer is expected, the answer is designated as a low quality answer.

[0017] In order to solve the above problem, claims 7 The invention described in the item (1) is a computer included in a response data processing device, which includes a response data acquisition means for acquiring a plurality of pieces of response data including content data indicating the content of the response to a questionnaire conducted via a network in a format in which an answer to a question is requested, and identification data for identifying the response, and an extraction means for extracting, from the acquired response data, low-quality response data corresponding to a low-quality answer that is an answer that falls under a low-quality standard that is set in advance as the answer. ,before a deletion means for deleting the extracted low-quality response data from the acquired response data based on the identification data indicating the extracted low-quality response data; and a generating means for generating new complementary answer data in a number corresponding to the number of deleted low-quality answer data, based on the answer data after the low-quality answer data has been deleted, to replace the deleted low-quality answer data. The program for processing answer data functions as a low-quality answer, and the low-quality criteria are configured to be any one or all of: (i) a first low-quality criterion: when the answers to the multiple-choice questions are mutually contradictory, each answer is designated as a low-quality answer; (ii) a second low-quality criterion: when the number of choices in the answer to the multiple-choice question is less than a predetermined number, the answer is designated as a low-quality answer; and (iii) a third low-quality criterion: when the answer from an answerer belonging to a different answer group from the answerer group from which the answer is expected, the answer is designated as a low-quality answer.

[0018] Claim 1, Claim 6 or claims 7According to the invention described in any one of the above, a plurality of pieces of answer data corresponding to a questionnaire conducted via a network in the form of requesting answers to questions are acquired, and low-quality answer data corresponding to low-quality answers are extracted and deleted from the answer data. At this time, any or all of the first to third low-quality criteria are used as low-quality criteria for extracting low-quality answers. Therefore, low-quality answers that should be excluded when collecting the answers to the questionnaire and when analyzing the answers thereafter can be effectively excluded. Furthermore, based on the answer data after the low-quality answer data has been deleted, new complementary answer data is generated in a number corresponding to the number of deleted low-quality answer data. Therefore, by using the answer data from which the low-quality answer data has been deleted, the low-quality answer data can be deleted and a necessary and sufficient number of answer data can be obtained.

[0019] In order to solve the above problem, the invention described in claim 2 is the response data processing device described in claim 1, wherein the first low-quality criterion is that the answers to each of the multiple questions included in one of the questionnaires are mutually contradictory, and the extraction means is configured to extract the low-quality response data corresponding to the low-quality answer that falls under the contradiction from the acquired response data.

[0020] According to the invention described in claim 2, in addition to the effects of the invention described in claim 1, the first low-quality criterion is that the answers to each of multiple questions included in a questionnaire are mutually contradictory, and low-quality answer data corresponding to the low-quality answers that fall under the contradiction is extracted from the answer data, so that low-quality answer data can be properly extracted and deleted using an appropriate first low-quality criterion.

[0021] In order to solve the above problem, the invention described in claim 3 provides a response data processing device described in claim 2, wherein the first low-quality criterion is either first low-quality criterion (a), which is the first low-quality criterion described in claim 2, or first low-quality criterion (b), which is that the questions included in the preliminary survey corresponding to the questionnaire and the questions included in the main survey corresponding to the questionnaire are similar and the answers to the questions are different from each other, and the extraction means extracts the low-quality answer information corresponding to the low-quality answers that fall into either of the above categories from the acquired response information, and each of the first low-quality criteria is scored so that the first low-quality criterion (b) is lower in quality as an answer than the first low-quality criterion (a), and the device further includes a determination means for determining the number of low-quality answer data to be deleted based on the scored first low-quality criteria and a predetermined deletion number criterion, and the deletion means is configured to delete the determined number of low-quality answer data from the acquired response data.

[0022] According to the invention described in claim 3, in addition to the effects of the invention described in claim 2, the first low-quality criterion (a) and the first low-quality criterion (b) are each scored, and a number of low-quality answer data determined based on each scored first low-quality criterion and a predetermined deletion number criterion is deleted from the answer data, so that low-quality answer data can be extracted and deleted using a more objective low-quality criterion.

[0027] In order to solve the above problem, claims 4 The invention described in claims 1 to 5 is 3In the response data processing device described in any one of the above, the third low-quality criterion is that the difference between the ratio of the number of respondents belonging to the respondent group from which the response is expected to be given to the total number of respondents and the ratio of the number of respondents belonging to the different respondent group who have given the response to the total number of respondents is equal to or greater than a predetermined standard, and the extraction means is configured to extract from the acquired response data the low-quality response data corresponding to the low-quality answers given by respondents belonging to the different respondent group that meet the third low-quality criterion.

[0028] Claim 4 According to the invention described in claim 1 to claim 2, 3 In addition to the effects of the invention described in any one of the above, when the third low-quality criterion is a difference between the proportion of respondents belonging to a respondent group from which an answer is expected and the proportion of respondents belonging to a respondent group different from the said respondent group and who have given the answer, the low-quality answer data corresponding to the low-quality answers given by respondents belonging to the different respondent group is extracted from the answer data. Therefore, the low-quality answer data can be properly extracted and deleted using an appropriate third low-quality criterion.

[0031] In order to solve the above problem, claims 5 The invention described in claim 1 In the response data processing device described in the above, the generating means is configured to newly generate the complementary response data by referring to the distribution of the entire acquired response data.

[0032] Claim 5 According to the invention described in claim 1 In addition to the effects of the invention described above, new complementary response data is generated by referring to the distribution of the entire original response data, so that it is possible to obtain response data that corresponds to the distribution of the original response data while also obtaining a necessary and sufficient number of responses. [Effects of the Invention]

[0033] As described above, according to the present invention, a plurality of pieces of answer data corresponding to a questionnaire conducted via a network in the form of requesting answers to questions are acquired, and low-quality answer data corresponding to low-quality answers are extracted and deleted from the answer data. At this time, any or all of the first to third low-quality criteria are used as low-quality criteria for extracting low-quality answers.

[0034] Therefore, low-quality responses that should be excluded when collecting survey responses and when subsequently analyzing the responses can be effectively excluded. Furthermore, based on the answer data after the low-quality answer data has been deleted, new complementary answer data is generated in a number corresponding to the number of deleted low-quality answer data. Therefore, by using the answer data from which the low-quality answer data has been deleted, the low-quality answer data can be deleted and a necessary and sufficient number of answer data can be obtained. [Brief explanation of the drawings]

[0035] [Figure 1] FIG. 10 is a diagram showing an example of a response by a contradiction respondent in the first embodiment. [Figure 2] FIG. 10 is a diagram showing an example of a response from a low responder in the first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of a statistically distributed low-quality response in the first embodiment. [Figure 4] 1 is a block diagram showing a schematic configuration of a questionnaire response processing device according to a first embodiment. [Figure 5] 4 is a flowchart showing a questionnaire response process according to the first embodiment. [Figure 6] FIG. 10 is a block diagram showing a schematic configuration of a questionnaire response processing device according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0036] Next, embodiments of the present invention will be described with reference to the drawings. Each of the embodiments described below is an embodiment in which the present invention is applied to a questionnaire response processing device that improves the quality of a questionnaire survey by deleting the responses by respondents described in items (A) to (C) above from the responses resulting from a question-and-answer survey conducted on users of terminal devices such as smartphones. In the following description, the responses by respondents described in items (A) to (C) above will be collectively referred to as "low-quality responses."

[0037] (I) First embodiment First, a first embodiment of the present invention will be described with reference to FIGS.

[0038] (A) About low quality answers First, before describing the questionnaire response processing device of the first embodiment, specific examples of low-quality responses that are the target of questionnaire response processing by the questionnaire response processing device will be described. Note that Fig. 1 is a diagram showing example responses of a contradictory respondent in the first embodiment, Fig. 2 is a diagram showing example responses of a low-responder in the first embodiment, and Fig. 3 is a diagram showing example responses of a statistically distributed low-quality response in the first embodiment.

[0039] As described above, the low-quality answers that are the target of the questionnaire answer processing in the first embodiment include answers by inconsistent answerers described in (A) above, answers by low responders described in (B) above, and answers with questionable statistical distribution described in (C) above. In the following explanation, answers by inconsistent answerers described in (A) above will be referred to as "inconsistent answers," answers by low responders described in (B) above will be referred to as "low-response answers," and answers with questionable statistical distribution described in (C) above will be referred to as "answers with questionable distribution."

[0040] The contradictory answers are "mutually contradictory answers to different questions" given by the contradictory answerer, and an example thereof is answer DX1 in which, in survey result D1 of a questionnaire survey regarding whether or not a drink (beverage) is known and whether or not the person has actually consumed it, the respondent selected "X" for the question item "I know," but selected "○" for the question item "I have drunk it," as shown in Figure 1. The contradictory answerer ultimately answered that "I have drunk that drink" despite "I don't know that drink," and these answers are mutually contradictory; in other words, answer DX1 shown in Figure 1 is a typical example of the contradictory answer.

[0041] Next, the low response response is a "simple (lazy) response indicating that none of the many options apply" given by the low responder. An example of this is response DX2, as shown in Figure 2, in which the respondent selected "none of these apply" (i.e., there is not a single beverage that they have ever drunk) in survey result D2 of a questionnaire survey asking whether or not the respondent has ever drunk a large number of beverages. It is generally difficult to imagine a low responder who gave such a low response response answer answering "none of these apply" even though as many beverages as shown in Figure 2 are listed as options. Therefore, the respondent who gave response DX2 in Figure 2 is likely to be a respondent (low responder) who, for example, found it troublesome to read all the options.

[0042] Finally, the distribution-questionable answers are answers given by respondents who are presumed to be giving false answers based on the statistical distribution. For example, as shown in Figure 3, the proportion of teenage males in the statistical distribution of all customer data for a certain product or service is extremely low (" 1.2"), but in survey results D3 of a questionnaire survey on that product or service, the most common answer given by teenage males is answer DX3 (shown as "16.5" in Figure 3). Respondents who give such distribution-questionable answers are considered to have doubts about their original statistical distribution and are therefore not considered to be legitimate respondents, and as mentioned above, they are respondents who are thought to be giving false answers for a certain purpose.

[0043] (B) Regarding the questionnaire response processing device of the first embodiment Next, a questionnaire response processing device of a first embodiment that deletes the low-quality responses from responses that are the results of a question-and-answer survey conducted for users of the terminal device will be specifically described with reference to Figs. 4 and 5. Fig. 4 is a block diagram showing the general configuration of the questionnaire response processing device of the first embodiment, and Fig. 5 is a flowchart showing the questionnaire response processing of the first embodiment. In Fig. 4, "database" is appropriately represented as "DB." In the flowchart shown in Fig. 5, the chronological flow of processing is indicated by solid arrows, and the data flow in each process is indicated by dashed arrows.

[0044] As shown in Figure 4, the questionnaire response processing device S of the first embodiment is specifically realized by, for example, a server computer or a personal computer, and is composed of a processing unit 1 consisting of a CPU or the like, a recording unit 2 consisting of an HDD (Hard Disk Drive) or an SSD (Solid State Drive) or the like, an operation unit 3 consisting of a keyboard, a mouse, etc., and a display 4 consisting of an LCD display, etc.

[0045] The processing unit 1 is made up of a contradictory respondent cleaning unit 10, a low responder cleaning unit 11, a statistical distribution cleaning unit 12, and a generation unit 13.

[0046] In this case, the contradictory respondent cleaning unit 10, low responder cleaning unit 11, statistical distribution cleaning unit 12, and generation unit 13 may be realized by a hardware logic circuit including a CPU or the like constituting the processing unit 1, or may be realized in software by the CPU or the like reading and executing a program corresponding to the questionnaire response processing of the first embodiment described later (see FIG. 5). In this case, the program may be pre-recorded in the recording unit 2 and read by the CPU or the like, or the program may be recorded in an external server device (not shown) and acquired by the CPU or the like via a network such as the Internet for use.

[0047] The processing unit 1 corresponds to an example of the "response data acquisition means" of the present invention, the inconsistent respondent cleaning unit 10, the low responder cleaning unit 11 and the statistical distribution cleaning unit 12 correspond to an example of the "extraction means" and an example of the "deletion means" of the present invention, respectively, and the inconsistent respondent cleaning unit 10 corresponds to an example of the "determination means" of the present invention. Furthermore, the low responder cleaning unit 11 corresponds to an example of the "classification means" and an example of the "second determination means" of the present invention, respectively, and the generation unit 13 corresponds to an example of the "generation means" of the present invention.

[0048] In the above configuration, the processing unit 1 of the questionnaire response processing device S is connectable to a survey result database 100 and a statistical database 102. The survey result database 100 is a database that stores response data, which is the results (responses) of a question-and-answer survey conducted for users of the terminal device, including content data indicating the content of the response and identification data for identifying the response or the respondent. The response data stored in the survey result database 100 includes response data of the low-quality responses (see FIGS. 1 to 3 ) and response data of responses that are not low-quality responses (i.e., normal, legitimate responses). In contrast, the statistical database 102 is a database that stores various general statistical data collected by, for example, public institutions or local governments. The data of the survey result database 100 and the statistical database 102 may be pre-recorded in the recording unit 2, or may be acquired from an external server device (not shown) via a network such as the Internet each time the questionnaire response processing of the first embodiment is executed.

[0049] Then, the questionnaire response processing device S uses the survey results database 100 and the statistical database 102 to delete the response data of low-quality responses from the response data constituting the survey results database 100. In addition, the survey response processing device S generates complementary response data in a number equivalent to the total number of deleted response data (for example, the same number as the number of deleted response data) based on the response data constituting the survey results database 100 after the response data of low-quality responses has been deleted, while referencing the response data constituting the survey results database 100 before the deletion. The method for generating the complementary response data will be described in detail later.

[0050] Thereafter, the questionnaire response processing device S adds the generated complementary response data to the post-deletion survey result database 100 to generate a new high-quality database 103. At this time, the number of response data constituting the newly generated high-quality database 103 will be the same as the number of response data constituting the survey result database 100 before the response data of the low-quality responses were deleted.

[0051] More specifically, first, the inconsistent answerer cleaning unit 10 of the processing unit 1 of the questionnaire answer processing device S uses a preset method for deleting inconsistent answers to delete the answer data of inconsistent answers from the answer data constituting the survey result database 100. This method for deleting inconsistent answers will be described in detail later.

[0052] Next, the low response person cleaning unit 11 of the processing unit 1 uses a preset method for deleting low response answers to delete the answer data of low response answers from the answer data that constitutes the survey result database 100. This method for deleting low response answers will also be described in detail later. The deletion of the answer data of low response answers by the low response person cleaning unit 11 is carried out in parallel with the deletion of the answer data of contradictory answers by the contradictory answerer cleaning unit 10.

[0053] Finally, the statistical distribution cleaning unit 12 of the processing unit 1 uses a preset method for deleting distribution-questioning answers to delete the answer data of the distribution-questioning answers from the answer data constituting the survey result database 100, based on the statistical data stored in the statistical database 102. This method for deleting distribution-questioning answers will also be described in detail later. The deletion of the answer data of the distribution-questioning answers by the statistical distribution cleaning unit 12 is carried out in parallel with the deletion of the answer data of contradictory answers by the contradictory answerer cleaning unit 10 and the deletion of the answer data of low-response answers by the low-response answer cleaning unit 11.

[0054] Then, after the response data for the contradictory responses, low-reflection responses, and distribution-questioning responses have been deleted from the response data constituting the survey result database 100, the response data is temporarily recorded in the recording unit 2 as a cleaned database 101.

[0055] Next, the generation unit 13 of the processing unit 1 generates the above-mentioned complementary answer data in the same number as the total number of answer data deleted by the contradictory answerer cleaning unit 10, the low responder cleaning unit 11, and the statistical distribution cleaning unit 12, based on the answer data that constitutes the survey results database 100 after the answer data of the low-quality answers has been deleted, while referring to the answer data that constitutes the survey results database 100 before the deletion. The generation unit 13 then adds the generated complementary answer data to the survey results database 100 after the answer data of the low-quality answers has been deleted. In this way, the generation unit 13 generates high-quality answer data in the same number as the number of answer data that should constitute the high-quality database 103 and that constitutes the survey results database 100 before the answer data of the low-quality answers has been deleted, and stores this in the high-quality database 103.

[0056] The operations required to execute each of the above-described functions are performed by the operation unit 3, and operation signals corresponding to the operations are output to the processing unit 1. Based on the operation signals, the processing unit 1 then executes the above-described series of functions. Information required to execute the functions is displayed, for example, on the display 4 and presented to the operator of the questionnaire response processing device S.

[0057] Next, the questionnaire response processing of the first embodiment executed by the questionnaire response processing device S will be specifically described with reference to FIGS.

[0058] The questionnaire response processing of the first embodiment, which is executed by the questionnaire response processing device S having the above-described functions, starts, for example, when a power switch (not shown) of the questionnaire response processing device S is turned on. Note that the response data that is the subject of the questionnaire response processing of the first embodiment is assumed to be stored in the survey results database 100 in advance.

[0059] When the survey response processing starts, first, the processing unit 1 of the survey result processing device S acquires the response data to be processed in the survey result processing from the survey result database 100 (step S1). Thereafter, the response data acquired from the survey result database 100 is output in parallel to the inconsistent respondent cleaning unit 10, the low responder cleaning unit 11, and the statistical distribution cleaning unit 12. Thereafter, for the response data acquired from the survey result database 100, the inconsistent respondent cleaning unit 10 executes the inconsistent response deletion process (inconsistent respondent cleaning process) described above (step S2), the low responder cleaning unit 11 executes the low response response deletion process (low responder cleaning process) (step S3), and the statistical distribution cleaning unit 12 executes the distribution-questionable response deletion process (statistical distribution cleaning process) (step S4). Then, the response data of the survey result database 100 from which the inconsistent responses, low response responses, and distribution-questionable responses have been deleted is temporarily stored in the cleaned database 101. In this case, if the number of response data (in other words, the number of respondents; the same applies below to the number of response data in each database) stored in the original survey results database 100 is 1,000, and 50 responses are deleted by the inconsistent respondent cleaning process (step S2), 100 responses are deleted by the low responder cleaning process (step S3), and 30 responses are deleted by the statistical distribution cleaning process (step S4), the number of response data stored in the cleaned database 101 will be 820.

[0060] (a) Regarding the contradictory respondent cleaning process of the first embodiment Here, the inconsistent answerer cleaning process of step S2 above will be described in more detail. In the inconsistent answerer cleaning process, a determination procedure (hereinafter, referred to as the "inconsistent answer determination procedure") is set in advance to determine inconsistent answers from answers indicated by the answer data acquired from the survey result database 100 in step S1 above. The inconsistent answer determination procedure is realized by the inconsistent answerer cleaning unit 10 reading and executing a program or the like for realizing the procedure, such as a program or the like pre-recorded in the recording unit 2. Note that the inconsistent answer determination procedure of the first embodiment is not fixed to the inconsistent answerer cleaning unit 10, but is changeable, for example, based on the attributes of the answerer to be excluded as an inconsistent answerer of the first embodiment. Furthermore, the multiple examples of the inconsistent answer determination procedure of the first embodiment shown below may be used alone, or two or more of the determination procedures may be used in combination.

[0061] A first example of such a procedure for discriminating between contradictory answers is a procedure for discriminating each answer as a contradictory answer when the answers to multiple questions included in (or corresponding to) one questionnaire survey are mutually contradictory, such as answer DX1 described using Figure 1.

[0062] Next, as a second example of a procedure for discriminating between contradictory answers, which is a more specific example of the first example of the procedure for discriminating between contradictory answers, when a question included in a preliminary survey (so-called screening survey) corresponding to the questionnaire survey from which the response data recorded in the survey results database 100 was obtained and a question included in a main survey corresponding to the questionnaire survey are of the same meaning, but the answers to each question are different (i.e., contradictory) from each other, the answers in the preliminary survey and the main survey are discriminated as the contradictory answers.

[0063] Next, as a third example of the procedure for discriminating inconsistent answers, the degree of inconsistency in an inconsistent answer may be assigned a score (score; the same applies below) in advance. More specifically, a score (score; the same applies below) corresponding to a respondent whose answer was determined to be inconsistent by the first example of the procedure for discriminating inconsistent answers may be set to, for example, "-100 points," and this information may be recorded in association with the respondent. Similarly, a score (score; the same applies below) corresponding to a respondent whose answer was determined to be inconsistent by the second example of the procedure for discriminating inconsistent answers may be set to "-70 points," and this information may be recorded in association with the respondent. Then, the scores for each respondent may be added up, and the respondent with a relatively larger score (negative number) after the addition may be ranked lower as an inconsistent respondent (in other words, closer to a normal respondent; the same applies below).

[0064] Finally, as a fourth example of the procedure for determining contradictory answers, the following method can be given as an example of how to actually delete the answer data of answers that have been determined to be contradictory in the first to third examples of the procedure for determining contradictory answers. (A) A method of deleting all response data of responses determined to be inconsistent from the survey result database 100, and excluding all respondents who provided inconsistent responses (i.e., inconsistent respondents) from the survey results. (i) A method of excluding inconsistent respondents whose answers were determined to be inconsistent in the third example from the survey results according to the score they received as inconsistent. More specifically, for example, a method of excluding the top 200 inconsistent respondents with the highest scores for inconsistent answers from the survey results, or a method of excluding the top 10 percent of inconsistent respondents with the highest scores from the survey results, may be used.

[0065] (b) Regarding the low reactant cleaning process of the first embodiment Next, the low-responder removal process of step S3 will be described in more detail. In the low-responder removal process, a discrimination procedure (hereinafter referred to as the "low-responder removal procedure") is set in advance to discriminate low-responder responses from among the responses indicated by the response data acquired from the survey result database 100 in step S1. The low-responder removal procedure is realized by the low-responder removal unit 11 reading and executing a program or the like for implementing the procedure, such as a program pre-recorded in the recording unit 2. The low-responder removal procedure of the first embodiment is not fixed to the low-responder removal unit 11, but is changeable based on, for example, the attributes of respondents who should be excluded as inconsistent respondents of the first embodiment. The following examples of the low-responder removal procedure of the first embodiment may be used individually, or two or more of the discrimination procedures may be used in combination.

[0066] A first example of such a procedure for discriminating low-response responses is a procedure in which the response data recorded in the survey results database 100 is analyzed using the so-called cluster analysis method, and the response of a respondent (low responder) who belongs to the cluster with the lowest response (i.e., the fewest specific options selected in the question) among the analyzed (classified) clusters is discriminated as a low-response response.

[0067] Next, as a second example of a procedure for discriminating low-response responses, there is a procedure for discriminating the response of a respondent (low responder) who answered "none of the options apply" to multiple (large number of) options in a question, such as answer DX2 explained using Figure 2, as a low-response response.

[0068] A third example of a procedure for determining low response responses is a procedure for determining that responses from respondents who answered "not applicable" to all of a plurality of questions (low responders) are low response responses.

[0069] Furthermore, as a fourth example of the procedure for identifying low-response responses, the degree of low response in a low-response response may be scored in advance. More specifically, the score corresponding to a respondent belonging to the lowest-response cluster in the first example of the procedure for identifying low-response responses may be recorded as "-90 points," and this information may be associated with the respondent. The score of a respondent whose answer was determined to be a low-response response in the second example of the procedure for identifying low-response responses may be recorded as "-30 points," and this information may be associated with the respondent. The score of a respondent whose answer was determined to be a low-response response in the third example of the procedure for identifying low-response responses (when the number of questions is three) may be recorded as "-150 points," and this information may be associated with the respondent. Then, the scores for each respondent may be added up, and respondents with relatively larger scores (negative numbers) after the addition may be ranked as respondents with lower low-response ratings.

[0070] Finally, as a fifth example of the procedure for determining low response responses, the following method can be given as an example of how to actually delete the response data of answers determined to be low response responses in the first to fourth examples of the procedure for determining low response responses. (A) A method of deleting all response data of responses determined to be low-response responses from the survey result database 100, and excluding all respondents who gave such low-response responses (i.e., low-responders) from the survey results. (i) A method of excluding low responders whose answers were determined to be low response answers in the fourth example above from the survey results according to the score of the low response answer. More specifically, for example, a method of excluding the top 200 low responders with the highest scores for low response answers from the survey results, or a method of excluding the top 10 percent of low responders with the highest scores from the survey results. (c) A method of excluding from the above-mentioned calculation targets respondents who answered "not applicable" to all three pre-set questions among respondents belonging to the lowest-response cluster in the first example of the procedure for determining low-response responses. (e) A method of setting an upper limit in advance for excluding the above-mentioned contradictory respondents and the above-mentioned low responders from the above-mentioned tabulation, and excluding respondents below that upper limit from the above-mentioned tabulation.

[0071] (c) Statistical Distribution Cleaning Process of the First Embodiment Next, the statistical distribution cleaning process of step S4 will be described in more detail. In this statistical distribution cleaning process, a discrimination procedure (hereinafter referred to as the "distribution-questioned answer discrimination procedure") is preset for discriminating distribution-questioned answers from among the answers indicated by the answer data acquired from the survey result database 100 in step S1 based on the statistical data stored in the statistical database 102. The distribution-questioned answer discrimination procedure is realized by the statistical distribution cleaning unit 12 reading and executing a program or the like for implementing the procedure, such as a program pre-recorded in the recording unit 2. The distribution-questioned answer discrimination procedure of the first embodiment is not fixed to the statistical distribution cleaning unit 12 but can be changed based on, for example, the attributes of respondents who are to be excluded as having given the distribution-questioned answer of the first embodiment. Furthermore, the following examples of the distribution-questioned answer discrimination procedure of the first embodiment may be used individually or in combination.

[0072] A first example of such a procedure for determining whether an answer is distribution-questionable is a procedure for determining whether an answer from a respondent who belongs to a statistically inappropriate segment (see symbol "DX3" in Figure 3) is a distribution-questionable answer based on the statistical data stored in the statistical database 102.

[0073] More specifically, for example, suppose the statistical data is collected by gender and age group in ten-year age segments. When the respondents whose response data is stored in the survey results database 100 are divided into gender and age group in ten-year age segments in the same way as the statistical data, if the number of respondents in each segment deviates by ±10% or more from the number of people belonging to the corresponding segment of the statistical data (for example, if only 3% of the statistical data show that males are in their teens, but 18% of the respondents whose response data is stored in the survey results database 100 are in their teens), the response of the male teenage respondent whose response data is stored in the survey results database 100 is determined to be the response with questionable distribution.

[0074] Next, as a second example of a procedure for determining distribution-questionable answers, the statistical distribution cleaning process of the first embodiment involves comparing segments of statistical data (hereinafter referred to as "statistical data segments") segmented in the same manner as in the first example above with segments of response data stored in the survey results database 100 (hereinafter referred to as "response data segments"). The response data belonging to each response data segment, whose response number does not correspond to the response number of the corresponding statistical data segment, is reduced according to the same rule until the number of respondents belonging to that response data segment is within a ±5% deviation range from the number of respondents in the corresponding statistical data segment. The procedure then includes determining whether the responses in the response data to be reduced are distribution-questionable answers. In this case, respondents whose responses are included in the response data segments to be deleted are targeted for reduction in order of their ranking as inconsistent respondents or low responders (i.e., respondents who are most likely to be inconsistent respondents or low responders).

[0075] After the low-quality answers are deleted by the inconsistent answerer cleaning process (step S2), the low responder cleaning process (step S3), and the statistical distribution cleaning process (step S4), the generation unit 13 of the processing unit 1 uses the answer data constituting the survey result database 100 after the answer data of the low-quality answers has been deleted, while referring to the answer data constituting the survey result database 100 before the deletion, for example, using the data generation method by the inventors of the present invention described in JP 2021-179865 A. The same number of complementary answer data as the total number of answer data of the deleted low-quality answers is newly generated (step S5). When generating the complementary answer data in this step S5, if the data generation method using AI described in JP 2021-179865 A is used, the quality of the entire answer data complemented by the complementary answer data can be improved.

[0076] More specifically, when 50 answers are deleted by the inconsistent answerer cleaning process (step S2), 100 answers are deleted by the low responder cleaning process (step S3), and 30 answers are deleted by the statistical distribution cleaning process (step S4), the generation unit 13 generates 180 new complementary answer data using the data generation method, etc. The generation unit 13 then acquires the answer data of the survey results database 100 after the answer data of the low-quality answers has been deleted from the cleaned database 101, and adds the complementary answer data generated in step S5 to the acquired answer data (step S6). As a result, the generation unit 13 generates high-quality answer data in the same number as the number of answer data constituting the survey results database 100 before the answer data of the low-quality answers was deleted, and stores this in the high-quality database 103 (step S6).

[0077] Thereafter, the processing unit 1 determines whether or not to terminate the questionnaire response processing of the first embodiment, for example, by an end operation on the operation unit 3 (step S7). If the determination in step S7 is to terminate the questionnaire response processing (step S7: YES), the processing unit 1 terminates the questionnaire response processing. On the other hand, if the determination in step S7 is to continue the questionnaire response processing, for example, with another survey results database 100 as the target (step S7: NO), the processing unit 1 returns to step S1 and continues the above-described processing with the other survey results database 100 as the target.

[0078] As described above, according to the questionnaire response processing by the questionnaire response processing device S of the first embodiment, multiple pieces of response data corresponding to a questionnaire survey conducted over a network in the form of requesting answers to questions are acquired (see step S1 in FIG. 5), and response data of low-quality answers are extracted from the response data and deleted (see steps S2 to S4 in FIG. 5). At this time, response data of contradictory answers (see step S2 in FIG. 5), response data of low-response answers (see step S3 in FIG. 5), and response data of distribution-questionable answers (see step S4 in FIG. 5) are respectively deleted as response data of low-quality answers, so that response data of low-quality answers that should be excluded when collecting the questionnaire responses and when analyzing the responses thereafter can be effectively excluded (deleted).

[0079] Furthermore, when the answer data of a contradictory answer is determined using the first example of the contradictory answer determination procedure to the fourth example of the contradictory answer determination procedure (see step S2 in Figure 5), the answer data of the contradictory answer can be extracted and deleted appropriately and properly.

[0080] In this case, when discriminating the answer data of contradictory answers using the third or fourth example of the contradictory answer discrimination procedure described above (see step S2 in Figure 5), answer data of low-quality answers can be extracted and deleted using objective criteria based on scoring.

[0081] Furthermore, when identifying response data of low-reaction responses using the first example of the low-reaction response discrimination procedure or the third example of the low-reaction response discrimination procedure (see step S3 in Figure 5), the response data of low-reaction responses can be properly extracted and deleted using appropriate criteria.

[0082] Furthermore, when the fourth example of the procedure for determining low-response responses is used, it is possible to delete the response data of low-response responses using a more objective criterion.

[0083] Furthermore, when determining the distribution question answer answer data by the distribution question answer determination procedure (see step S4 in FIG. 5), the distribution question answer answer data can be properly extracted and deleted using appropriate criteria.

[0084] Furthermore, based on the answer data after the answer data of the low-quality answers has been deleted, new complementary answer data of complementary answers in the same number as the number of deleted low-quality answers is generated (see steps S5 and S6 in Figure 5).By using the answer data after the answer data of the low-quality answers has been deleted, it is possible to construct a high-quality database 103 in which the answer data of the low-quality answers has been deleted and which has a necessary and sufficient number of answer data.

[0085] In this case, if a data generation method using AI is used to generate the complementary answer data, it is possible to improve the quality of the entire answer data complemented by the complementary answer data (i.e., the entire answer data of the complemented answers in the questionnaire survey). Furthermore, the number of complementary answer data generated in steps S5 and S6 may be the same as the number of deleted low-quality answers, or may be a number that is smaller than the number of deleted low-quality answers and corresponds to the number of deleted low-quality answers.

[0086] Furthermore, when generating the complementary response data, the complementary response data is newly generated by referring to the distribution in the original survey result database 100, so that it is possible to construct a high-quality database 103 that corresponds to the distribution in the original survey result database 100 and has a necessary and sufficient number of responses.

[0087] In the questionnaire response processing of the first embodiment shown in Figure 5, the case where the inconsistent respondent cleaning processing (step S2), the low respondent cleaning processing (step S3), and the statistical distribution cleaning processing (step S4) are executed in parallel has been described. However, these cleaning processing may alternatively be executed serially, that is, in the order of the inconsistent respondent cleaning processing (step S2) → the low respondent cleaning processing (step S3) → the statistical distribution cleaning processing (step S4), in chronological order.

[0088] Furthermore, in the questionnaire response processing of the first embodiment described above, the answer data of low-quality answers to be deleted in the questionnaire response processing has been described as the answer data of the above-mentioned contradictory answers, answer data of low-response answers, or answer data of answers with questionable distribution. However, the answer data of low-quality answers to be deleted may also include answer data of low-time answers, in which the answer time required by the respondent to the questionnaire survey to answer the included questions is shorter than a preset threshold, in addition to answer data such as the above-mentioned contradictory answers. In this case, it is sufficient to configure the system to delete answer data that corresponds to any or all of the above-mentioned answer data of contradictory answers, answer data of low-response answers, answer data of answers with questionable distribution, and answer data of the above-mentioned low-time answers.

[0089] (II) Second embodiment Next, a second embodiment of the present invention will be described with reference to Fig. 6. Fig. 6 is a block diagram showing the general configuration of a questionnaire response processing device of the second embodiment.

[0090] In the questionnaire response processing of the first embodiment described above, response data of low-quality responses is deleted from the survey results database 100, and new complementary response data is generated in the same number as the deleted number, thereby generating high-quality response data in the same number as the number of response data constituting the survey results database 100 before the response data of low-quality responses was deleted, and storing the generated data in the high-quality database 103. In contrast, in the database generation processing of the second embodiment described below, new response data is further generated by the generation unit 13, and a new large-scale database is generated, for example as a virtual market database, with the number of response data increased even more than the number of response data in the high-quality database 103 of the first embodiment.

[0091] The hardware configuration of the questionnaire response processing device of the second embodiment is basically the same as the hardware configuration of the questionnaire response processing device S of the first embodiment. Therefore, in the following explanation, the same components as those in the questionnaire response processing device S are assigned the same component numbers, and detailed explanations will be omitted.

[0092] As shown in Figure 6, the generation unit 13A of the processing unit 1A of the questionnaire response processing device S1 of the second embodiment, in addition to the functions of the generation unit 13 of the first embodiment, further generates a predetermined number of new response data corresponding to the number of high-quality data 103, for example, in a manner similar to that of the generation unit 13, based on the response data of the high-quality database 103, while referring to, for example, the distribution of statistical data constituting the statistical database 102.

[0093] Thereafter, the generation unit 13A combines the newly generated response data with the response data already stored in the high-quality database 103 (i.e., expands the response data) to generate response data that constitutes the large-scale database 110 of the second embodiment, and stores the response data in the large-scale database 110.

[0094] The questionnaire response processing of the second embodiment described above has the same effect as the questionnaire response processing of the first embodiment, and also has the effect of enabling the large-scale database 110 to be constructed easily.

[0095] In addition, according to the questionnaire response processing of the second embodiment described above, when a large-scale virtual market database is constructed by expanding response data using complementary response data (e.g., generated using AI) similar to that of the first embodiment, the quality of the data contained in the virtual market database can also be improved. [Industrial Applicability]

[0096] As explained above, the present invention can be used in the field of processing response data that constitutes a database, and particularly when applied to the field of improving the quality of the response data, a particularly remarkable effect can be obtained. [Explanation of symbols]

[0097] 1, 1A processing section 2 Recording section 3 Control section 4. Display 10. Contradictions Respondent Cleaning Department 11 Low Responder Cleaning Department 12 Statistical Distribution Cleaning Department 13, 13A generation section 100 Survey Results Database 101 Cleaned Database 102 Statistical Database 103 High-Quality Database 110 Large Databases S, S1 Questionnaire response processing device D1, D2, D3 survey results DX1, DX2, DX3 Answer

Claims

1. a response data acquisition means for acquiring a plurality of response data including content data indicating the content of the response to a questionnaire conducted via a network in a format requesting an answer to a question, and identification data for identifying the response; an extraction means for extracting, from the acquired answer data, low-quality answer data corresponding to a low-quality answer that is an answer that falls under a low-quality standard that is preset as the answer; a deletion means for deleting the extracted low-quality response data from the acquired response data based on the identification data indicating the extracted low-quality response data; a generating means for generating new complementary answer data in a number corresponding to the number of deleted low-quality answer data, based on the answer data after the low-quality answer data has been deleted, to replace the deleted low-quality answer data; Equipped with The low quality standard is: (i) a first low-quality criterion: when the answers to a plurality of the questions are mutually contradictory, the answers are determined to be low-quality answers; (ii) a second low-quality criterion: determining an answer to the multiple-choice question in which the number of selections is less than a predetermined number as the low-quality answer; and (iii) Third low-quality criterion: determining that the answer given by a respondent belonging to a different respondent group from the respondent group from which the answer is expected is a low-quality answer; Response data processing device characterized in that it is any one or all of the above.

2. 2. The response data processing device according to claim 1, the first low-quality criterion is that the answers to the questions included in one of the questionnaires are mutually contradictory; The answer data processing device is characterized in that the extraction means extracts the low-quality answer data corresponding to the low-quality answer corresponding to the contradiction from the acquired answer data.

3. 3. The response data processing device according to claim 2, The first low quality criterion is The first low quality criterion (a) according to claim 2, or A first low-quality criterion (b) is that the questions included in the preliminary survey corresponding to the questionnaire and the questions included in the main survey corresponding to the questionnaire are of the same meaning, and the answers to the questions are different from each other; Either The extraction means extracts low-quality answer information corresponding to any of the low-quality answers from the acquired answer information, Each of the first low quality standards is scored so that the first low quality standard (b) is lower in quality as the answer than the first low quality standard (a), The method further includes determining means for determining the number of the low-quality response data to be deleted based on each of the first low-quality criteria scored and a predetermined deletion number criterion; The answer data processing device, wherein the deletion means deletes the determined number of low-quality answer data from the acquired answer data.

4. 4. The response data processing device according to claim 1, The third low-quality criterion is that the difference between the ratio of the number of respondents belonging to the group of respondents from which the answer is expected to be given to the total number of respondents and the ratio of the number of respondents belonging to the different group of respondents who have given the answer to the total number of respondents is equal to or greater than a predetermined standard; The answer data processing device is characterized in that the extraction means extracts the low-quality answer data corresponding to the low-quality answers by respondents belonging to the different answer group that fall under the third low-quality criterion from the acquired answer data.

5. 2. The response data processing device according to claim 1, The answer data processing device, wherein the generating means generates the complementary answer data by referring to the distribution of the entire acquired answer data.

6. A response data processing method executed in a response data processing device including a response data acquisition means, an extraction means, a deletion means, and a generation means, a response data acquisition step of acquiring, by the response data acquisition means, a plurality of response data pieces, each of which includes content data indicating the content of the response to a questionnaire conducted via a network in a format requesting an answer to a question, and identification data for identifying the response; an extraction step of extracting, from the acquired answer data, low-quality answer data corresponding to a low-quality answer that is an answer that falls under a low-quality standard that is preset as the answer by the extraction means; a deleting step of deleting the extracted low-quality response data from the acquired response data by the deleting means based on the identification data indicating the extracted low-quality response data; a generating step of generating, by the generating means, new complementary answer data in a number corresponding to the number of deleted low-quality answer data, based on the answer data after the low-quality answer data has been deleted, to replace the deleted low-quality answer data; Including, The low quality standard is: (i) a first low-quality criterion: when the answers to a plurality of the questions are mutually contradictory, the answers are determined to be low-quality answers; (ii) a second low-quality criterion: determining an answer to the multiple-choice question in which the number of selections is less than a predetermined number as the low-quality answer; and (iii) Third low-quality criterion: determining that the answer given by a respondent belonging to a different respondent group from the respondent group from which the answer is expected is a low-quality answer; The answer data processing method is characterized in that any one or all of the above is performed.

7. A computer included in a response data processing device, a response data acquisition means for acquiring a plurality of response data including content data indicating the content of the response to a questionnaire conducted via a network in a format requesting an answer to a question, and identification data for identifying the response; an extraction means for extracting, from the acquired answer data, low-quality answer data corresponding to a low-quality answer that is an answer that falls under a low-quality standard that is preset as the answer; a deletion means for deleting the extracted low-quality response data from the acquired response data based on the identification data indicating the extracted low-quality response data; and a generating means for generating new complementary answer data in a number corresponding to the number of deleted low-quality answer data, based on the answer data after the low-quality answer data has been deleted, to replace the deleted low-quality answer data; A response data processing program that functions as The low quality standard is: (i) a first low-quality criterion: when the answers to a plurality of the questions are mutually contradictory, the answers are determined to be low-quality answers; (ii) a second low-quality criterion: determining an answer to the multiple-choice question in which the number of selections is less than a predetermined number as the low-quality answer; and (iii) Third low quality criterion: Answers that belong to a different group of respondents than the group of respondents from whom the answer is expected. determining the answer by the user as the low quality answer; A program for processing answer data, characterized in that it is any one or all of the above.

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