CN113782188B - Multi-dimensional test data processing method of SJT situation type children psychological assessment system - Google Patents
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Abstract
The application discloses a multi-dimensional test data processing method of an SJT situation type children psychological assessment system, which comprises the following steps: obtaining test results of a user on preset multidimensional test questions; statistically classifying the test results based on a potential class analysis method; and distinguishing the statistical classification result by using a bipartite mode to obtain a distinguishing result. The application measures the psychological health of the subject through multidimensional testing, and bridges different testing modules by using a statistical method. The multi-dimensional test data processing method of the SJT contextual child psychological assessment system provided by the application can be used for primarily assessing the psychological health state of the subject, so as to achieve the early warning function. Meanwhile, aiming at psychological disorder with early warning, the assessment and the interview can be further carried out.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a multi-dimensional test data processing method of an SJT situation type children psychological assessment system.
Background
Mental health is a complex concept, and the content, structure and composition involved are not absolutely single planes, possibly including emotional aspects, cognitive aspects, self-concepts, compressive resistance, toughness …, etc., and if measured by a single test, it is difficult to peep through the mental health overview of children. The more structural measures can be made for the content related to mental health, the more complete the mental health of the subject will be known. However, the current psychological test technology usually only performs a test for a certain dimension, so that although the dimension can be accurately tested, the influence of other dimensions is ignored, and thus, the defect of insufficient test precision exists in the whole.
Disclosure of Invention
The embodiment of the application provides a multi-dimensional test data processing method, a device, computer equipment and a storage medium of an SJT situation type children psychological assessment system, which aim at providing accurate psychological assessment and early warning effects for users.
In a first aspect, the present embodiment provides a method for processing multidimensional testing data of an SJT contextual child psychological assessment system, including:
obtaining test results of a user on preset multidimensional test questions;
statistically classifying the test results based on a potential class analysis method;
and distinguishing the statistical classification result by using a bipartite mode to obtain a distinguishing result.
In a second aspect, the present embodiment provides a multi-dimensional test data processing device of an SJT contextual child psychological assessment system, including:
the result acquisition unit is used for acquiring test results of a user on a preset multi-dimensional test question;
the statistical classification unit is used for statistically classifying the test results based on a potential category analysis method;
and the result distinguishing unit is used for distinguishing the statistical classification result by utilizing a bisection mode to obtain a distinguishing result.
In a third aspect, the present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the multi-dimensional test data processing method of the SJT contextual child psychological assessment system according to the first aspect when executing the computer program.
In a fourth aspect, the present embodiment provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, where the computer program when executed by a processor implements the multi-dimensional test data processing method of the SJT contextual child psychological assessment system according to the first aspect.
The embodiment of the application provides a multi-dimensional test data processing method, a device, computer equipment and a storage medium of an SJT situation type children psychological assessment system, which comprises the following steps: obtaining test results of a user on preset multidimensional test questions; statistically classifying the test results based on a potential class analysis method; and distinguishing the statistical classification result by using a bipartite mode to obtain a distinguishing result. In this embodiment, the mental health of the subject is measured through multidimensional testing, and a statistical method is applied to bridge different testing modules. Through the multidimensional testing data processing method of the SJT contextual child psychological assessment system provided by the embodiment, the psychological health state of the subject can be primarily assessed, and then the early warning function is achieved. Meanwhile, the psychological disorder (disease) with early warning can be further evaluated and interviewed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a multi-dimension test data processing method of an SJT contextual child psychological assessment system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a sub-flowchart of a multi-dimensional test data processing method of an SJT contextual child psychological assessment system according to an embodiment of the present application;
FIG. 3 is a schematic block diagram of a multi-dimensional test data processing device of an SJT contextual child psychological assessment system according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a multi-dimensional test data processing device of an SJT contextual child psychological assessment system according to an embodiment of the present application;
fig. 5 is an exemplary schematic diagram of a multi-dimensional test data processing method of an SJT contextual child psychological assessment system according to an embodiment of the present application;
fig. 6 is a schematic diagram of another example of a multi-dimension test data processing method of an SJT contextual child psychological assessment system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a flow chart of a multi-dimension test data processing method of an SJT contextual child psychological assessment system according to an embodiment of the present application, which specifically includes: steps S101 to S103.
S101, obtaining a test result of a user on a preset multidimensional test question;
s102, carrying out statistical classification on the test results based on a potential category analysis method;
s103, distinguishing the statistical classification result by using a bipartite mode to obtain a distinguishing result.
In this embodiment, a multi-dimensional test question is preset to perform a psychological test on the user (or the subject), and the test result answered by the user is obtained. And then carrying out statistical classification on the test results through a potential category analysis method, and further carrying out result differentiation through a binary method to obtain a differentiation result, wherein the differentiation result is a processing result of the multi-dimensional test data.
Mental health is a complex concept, and the content, structure and composition involved are not absolutely single planes, possibly including emotional aspects, cognitive aspects, self-concepts, compressive resistance, toughness …, etc., and if measured by a single test, it is difficult to peep through the mental health overview of children. The more structural measures can be made for the content related to mental health, the more complete the mental health of the subject will be known. Therefore, the embodiment measures the mental health of the child through the multidimensional testing, and bridges different testing modules by applying the statistical method. By the psychological test method provided by the embodiment, the psychological health state of the subject can be estimated preliminarily, and then the early warning function is achieved. Meanwhile, the psychological disorder (disease) with early warning can be further evaluated and interviewed.
In an embodiment, the preset multi-dimensional test question is a multi-dimensional situation type animation test question, and the situation type animation test question includes a self-concept test question, an emotion mastering test question and a cognitive development test question.
In this embodiment, the multi-dimensional contextual animation testing problem refers to performing a contextual test on a user, wherein the contextual test refers to a method for the user to solve a problem in a designed context, so as to observe conditions such as knowledge of the context, adaptation of the context, utilization of the context, and the like, and further present the solution of the problem. Through the pre-designed situation, the reaction of the user in the situation is observed, and the solution method corresponds to different psychological tendencies, so that the internal overall situation of the user is estimated. The variety of the situation test is very wide, the expression form can be various, and no matter what type of the situation test, the basic principle of psychological test should be followed, so that the test degree is standardized as much as possible. It will be appreciated that the scenario test described in this embodiment is particularly suited for children, as the result of the concentration of the child. In particular, the length of time that the child is attentive is 15-45 minutes, while when engaging in higher-order concentration activities (e.g., playing puzzles, writing, etc.), the time is more likely to be reduced to 6-10 minutes. Therefore, the present embodiment adopts the contextual theme design, so that the child can make appropriate response after understanding through daily familiar scenes, and meanwhile, the child can focus on the testing process by adopting the animation presentation. In this example, it is not a direct measurement of whether or not a child has depression, anxiety, or a psychological condition such as tendency to self-close. Instead, variations such as emotion, self-concept, etc. associated with these special behaviors are employed, firstly to measure whether a child has mental health problems; and secondly, whether the potential psychological symptoms exist can be further detected. Emotion mastering, self-conception and cognitive development to evaluate appear to be very abstract concepts. However, when these abstract parts are converted into the examination mode with situation and picture, the children react in the whole scene, and the feeling that the children fear to be examined and measured does not exist, so that the mental ideas and feelings can be presented more intuitively.
In addition, the present embodiment forms three structural planes of self-concept testing, emotion mastering testing and cognitive development testing to form a child mental health ICE testing.
Wherein, the self concept is accumulated from the beginning of the period of infants, and on the basis, the unique personality traits of the future are extended. In addition, children develop more and more sophisticated self-concepts and thereby begin to evaluate their own features. According to the study, those with positive self-concepts had a lower anxiety level and those with negative self-concepts had a higher anxiety level, and anxiety was considered as the basis of psychological disorders; in addition, when an individual experiences behavior that is inconsistent with his own concept, there is anxiety and tension, and in order to protect himself and counter the threat, he or she repudiates and distorts his experience, creating a distorted self-concept, and as a result he or she cannot learn not only himself or herself, but also other people, and causing a personal maladaptation. Meanwhile, according to research, the self-help children have the advantages that the self-help children can be realized by the high self-help concepts, meanwhile, the self-help children are also known to be satisfactory to most of the self-help children, so that guiding, explaining and expected effects can be generated on the mental health development of the children, and the children are more forward developed. This is why in this embodiment, in particular, a self-conceptual test is put on one of the faces.
For "cognitive development", the concentration of the child is the "basic work" of learning, and the improvement of concentration can greatly improve effective learning. Therefore, the cognitive development takes concentration as main evaluation content, and the concentration degree of the children in learning can be known through the embodiment, and meanwhile, whether further assistance is needed to be provided or not. Therefore, the present embodiment can know the concentration status of the child, and the user can clearly know whether the development level is achieved after comparing the normal model, so that a correct intervention mode can be provided for the problem. This is why in this example, in particular, the concentration test in cognitive development is put on the second face.
The detection of emotion mastering can know which mode is adopted to treat the emotion when the child faces negative stimulus, and the child is timely assisted to adjust the correct emotion expression mode after knowing the conventional mode. The animation design of the embodiment describes events which can happen to a child in daily life and school activities, and enables the child to choose different solutions through scenes which the child is familiar with, and different emotion strategies are displayed in the directions behind the different solutions. After a series of emotion mastering test questions are done, the child can know which mode is adopted to process the emotion when facing negative stimulus, and then whether the mode belongs to an adaptive strategy or an inadaptive strategy is distinguished. In addition, strategies such as "evasion", "inhibition", and the like, which take into account inadaptability, have a moderately positive correlation with depression and anxiety disorders, because "evasion" and "evasion" while reducing expression of emotion in a short period of time, do not contribute to effectively reducing negative emotion of an individual. As for the adaptive strategies section, "accept", "problem solve", "re-evaluate" strategies are most often of low negative relevance to the relevance of anxiety-bearing disorders. Such studies reflect two things, first, children develop different mood adjustment strategies; second, the "appearance" of an inadaptation strategy may have a less favorable impact on the mental health of the child than the "lack" of an inadaptation strategy. Thus, testing for emotions has an impact on children in terms of how adults help children adjust their emotional expressions, otherwise there is a great impact on mental health once children are permanently accustomed to using an unsuitable emotional strategy. This is why, in this embodiment, in particular, a test for emotion mastery is put in the third of these. Although the mental health of the subject can be more completely understood if more structural measurements can be made for the content related to the mental health, in practice, all tests cannot be implemented on the child subject, so that the relevant tests need to be selected and further analyzed through the back-end data to find out the key indexes, and the aim is to apply the technologies of tests, statistics and the like to achieve the effective mental health measurement of the child. Therefore, this embodiment integrates three structural planes of self concept (identity), emotion mastering (motion), and cognitive development (Cognition), and becomes a child mental health ICE test.
In one embodiment, as shown in fig. 2, the step S102 includes: steps S201 to S203.
S201, constructing a potential category model for each situation type animation testing problem through a potential category analysis method;
s202, selecting an optimal category model from all potential category models by using a mode selection criterion;
s203, carrying out statistical classification on the test results based on the optimal category model.
In this embodiment, a potential class analysis method is cited, a potential class model is established according to whether each question of the subject is answered, an optimal class model is selected from a plurality of potential class models according to a mode selection criterion, and then the subject is classified into psychological disorders (diseases). In the psychological test process, each test has its purpose, however, mental health is a compound concept, and in no way only a single index or test can be completely described. Therefore, under such a multiple concept, a psychological wellness scale or test usually includes a plurality of structural concepts, so that the embodiment is to apply the statistical analysis software to the reaction group types of the three structural modules to find the most suitable potential class model (i.e. the best class model) to statistically classify the test result, so as to provide an important basis for the mental health of the child to be correctly pointed to mental disorder (disease). The assumption of the potential category analysis method (patent ClassAnalysis, LCA) is that the potential feature is to use categories (category) as variants, and the characteristics of the category variants according to the questions can be hierarchical continuity, parallel coexistence without hierarchical association, and the potential feature is more consistent as an input variant for psychological tests which often use a measuring scale as a score. The basic principle of this embodiment is: in the same potential characteristics, the test questions are assumed to accord with local independence, so that the proper group number and structure can be searched according to the response group type and mode of the answer questions of the subjects and the adaptation degree of data. As shown in fig. 5, three small samples are included in the total sample of the psychological test and correspond to potential category 1 (self concept), potential category 2 (emotion mastery) and potential category 3 (cognitive development), respectively. Wherein potential class 1 may be associated with other psychological disorders directed to depression, anxiety, ADHD, and the like; potential class 2 may be associated with psychological disorders directed to tui, ADHD, and autism; potential class 3 may be associated with other psychological disorders directed to anxiety, violent tendencies, autism, and the like.
In an embodiment, the modulo criterion is a bayesian information criterion.
Since the main purpose of psychological tests is: with or without psychological disturbances, and to distinguish what is the problem. Therefore, how to classify possible psychological disorders (diseases) according to the test results of the subject, so that classification and selection of models is of great importance in application, and good models can provide correct variable relationships or provide better predictions, and conversely can lead to misleading of variable relationships. After inputting data, through the statistical analysis software, the first report is an adaptation index, wherein AIC (Akaike information criterion, red pool information criterion) and BIC (Bayesian information criterion ) can be used to determine the specific group number division, and the smallest AIC and BIC is the preferred group division. The early most part starts with the selection of the interpretation variables in the regression model. In this embodiment, BIC is used as a mode selection criterion, and BIC is a mode selection method with consistency, that is, when the candidate mode includes a true mode, the probability of the mode selected by BIC being the correct mode approaches 1, otherwise, there is no problem of degree adaptation, because the coefficient of the penalty function of BIC is log N, which is generally greater than the coefficient 2 of the penalty function of AIC.
In one embodiment, the step S103 includes:
acquiring a cut-off number according to the statistical classification result;
determining a first score above the cutoff score and a second score below the cutoff score based on the cutoff score;
and respectively transcoding the first fraction and the second fraction, and taking the transcoding result as a psychological test result.
In this embodiment, before the LCA, since the original score of each variant belongs to the continuous variant, when the LCA is performed with the original score, if the potential class exceeds 16 classes, more classes can be still separated, but a brief classification result cannot be provided. In other words, LCA is performed with a plurality of continuous variations, and sometimes an unintelligible or excessive number of categories and category group types are obtained. Thus, data transformation helps to obtain a simple classification result. Meanwhile, the main purpose of the embodiment is to distinguish whether children have mental disorder problems or not, and the method belongs to bisection segmentation. In summary, the present embodiment adopts a binary method to convert each variable score into a binary score ("have" or "have") form, i.e. find the truncated score according to the statistics of each data orientation. Above the cut-off score, it is transcoded to 1 (i.e. "there") and below the cut-off score, it is transcoded to 0 (i.e. "there"). Further, it is determined that there is no psychological disorder problem for the test result of the transcoding 0, and that there is a psychological disorder problem for the test result of the transcoding 1.
For example, in a self-concept test, a total of 10 questions are used to measure the degree score of the subject in the "behavior" direction, each question is answered by "yes" or "no", when the answer is "yes", a score is obtained, otherwise, a score is obtained by zero (the opposite question is given an opposite score), so the degree score interval is 0 to 10, if the continuous variable term is used for analysis, a plurality of categories can run out due to excessive variables, a brief classification result cannot be provided, and the inventor converts the data. From the pre-test sample analysis results, subjects below 7 scores were in the normal range for about 96% of the "behavioural" score, while exceeding 7 scores showed a very high probability of behavioural problems at that score. Therefore, 7 points are used as the cut-off numbers, namely, 0-7 points and 8-10 points are used as the two groups of points. Other factors in cognitive development and emotion management all depend on the same process to obtain different cut scores.
In an embodiment, the multi-dimensional test data processing method of the SJT contextual child psychological assessment system further includes:
the self concept, emotion mastering and cognitive development are respectively used as potential variates, and the self concept testing questions, emotion mastering testing questions and cognitive development testing questions corresponding to the self concept, emotion mastering and cognitive development are respectively used as observation variates;
based on the observed variation item and the potential variation item, constructing an analysis pattern diagram for self-concept, emotion mastering and cognitive development respectively by utilizing an optimal category model;
and acquiring verification parameters of the potential variation and the observation variation according to the analysis mode diagram, and judging the performance of the optimal category model according to the verification parameters.
In this embodiment, performance testing is performed on the selected optimal category model, specifically, self-concept, emotion mastering and cognitive development are used as potential variations, and meanwhile, self-concept testing questions, emotion mastering testing questions and cognitive development testing questions are used as observation variations. By constructing an analysis pattern graph for potential variations and observation variations, corresponding verification parameters can be obtained, thereby determining the optimal class model performance parameters. For example, taking a child emotion grasp test as an example, in the verification factor mode, as shown in fig. 6, "emotion grasp" is taken as a potential variation item, represented by ER, and seven of a to G contextual topics are taken as observation variation items (a to G represent seven contextual topics related to emotion grasp in the present embodiment). Before the adaptation of the test mode, it is checked whether the emotion grasp test of children violates the estimation, and as can be seen from tables 1 and 2, the regression coefficients of the emotion grasp potential variation items to the observation variation items are all 0.05, and the error variation parts are all not negative and reach significant level. For the normalized parameter estimation portion, except for F, the absolute values of the correlations between the parameters that all meet the estimation cannot be too close to the 1 standard all coefficients, the standard error is smaller than 1, the factor loading is also greater than 0.3, and the standard error is not significantly excessive.
*P<0.05
TABLE 1
*P<.05
TABLE 2
Table 3 shows the results of overall pattern adaptation test for analyzing the verifiability factors of children emotion mastering test, and the pattern χ is known in the absolute adaptation part 2 At a level of 20.21, p=0.12, less than 0.05, the display mode is adapted to the observed data; as for other indexes, GFI (goodness of fit index) =0.99, AGFI (adjusted goodness of fit index) =0.98, all greater than 0.90, rmsea (root mean square of approximation error) of 0.03, less than standard 0.05, belonging to a good fit, and finally normalized chi-square value of 1.44, meeting the standard of less than 3. Then, in the value added fitness part, NFI (reference fitting index), NNFI (non-reference fitting index), CFI (comparative fitting index) are respectively 0.94, 0.97, 0.98, which are all greater than 0.90 standard. Finally, in the simple adaptation degree, PNFI (model reduction degree index) is 0.63 and is larger than 0.50 standard, and PGFI (simple fitting goodness index) is0.50, although not greater than 0.50 standard. But close to the standard edge to be acceptable. From the three kinds of fitness indexes, the children emotion mastering test mode has good fitness and certain construction efficiency.
TABLE 3 Table 3
In an embodiment, the multi-dimensional test data processing method of the SJT contextual child psychological assessment system further includes:
and constructing a psychological data report based on the distinguishing result, and associating the psychological data report with different psychological disorders.
In this embodiment, psychological test results are associated with psychological disorders by constructing a psychological data report. For example, according to self-concepts (category 4), emotion mastery (category 8) and cognition development (category 3) in the psychological data report, the psychological disorders (diseases) common to children are grouped, so that the psychological disorders (diseases) in the same group have similar characteristics. Therefore, these category variants are put into the explicit variables and analyzed by the statistical software. The conditional probability of report data can be obtained through analysis, which shows the probability that the self concept (class 4), emotion mastery (class 8) and cognitive development (class 3) are certain psychological disorders (diseases), so that the probability of psychological disorders (diseases) common to children such as attention deficit disorder (ADHD), asian-Sibert, autism and the like can be obtained. I.e. the technology of how the ICE test links to mental disorders (diseases) of children in this embodiment. Based on the result of the corresponding psychological disorder (disease), the interview tool of the psychological disorder (disease) can be further used to evaluate and diagnose the testee to know whether the psychological disorder (disease) is diagnosed, the possibility is diagnosed and the non-diagnosed.
Fig. 3 is a schematic block diagram of a multi-dimensional test data processing device 300 of an SJT contextual child psychological assessment system according to an embodiment of the present application, where the device 300 specifically includes:
a result obtaining unit 301, configured to obtain a test result of a user on a preset multidimensional test question;
a statistical classification unit 302, configured to statistically classify the test result based on a potential class analysis method;
the result differentiating unit 303 is configured to differentiate the statistical classification result by using a binary method, so as to obtain a differentiated result.
In an embodiment, the preset multi-dimensional test question is a multi-dimensional situation type animation test question, and the situation type animation test question includes a self-concept test question, an emotion mastering test question and a cognitive development test question.
In one embodiment, as shown in fig. 4, the statistical classification unit 302 includes:
a model construction unit 401, configured to construct a potential category model for each scenario type animation test question through a potential category analysis method;
a model selection unit 402, configured to select an optimal class model from all the potential class models by using a modulo criterion;
and a model classification unit 403, configured to statistically classify the test result based on the best class model.
In an embodiment, the modulo criterion is a bayesian information criterion.
In an embodiment, the result differentiating unit 303 includes:
the cut-off number acquisition unit is used for acquiring the cut-off number according to the statistical classification result;
a score determining unit configured to determine a first score located above the cut score and a second score located below the cut score based on the cut score;
and the score transcoding unit is used for transcoding the first score and the second score respectively and taking the transcoding result as a psychological test result.
In one embodiment, the multi-dimensional test data processing device 300 of the SJT contextual child psychological assessment system further comprises:
a variation setting unit, configured to take a self concept, emotion grasp and cognitive development as potential variations, and take a self concept test question, an emotion grasp test question and a cognitive development test question, which correspond to the self concept, emotion grasp and cognitive development, as observation variations, respectively;
the pattern diagram construction unit is used for constructing an analysis pattern diagram for self concept, emotion mastering and cognitive development respectively by utilizing the optimal category model based on the observation variation item and the potential variation item;
and the performance verification unit is used for acquiring verification parameters of the potential variation and the observation variation according to the analysis pattern diagram, and judging the performance of the optimal category model through the verification parameters.
In one embodiment, the multi-dimensional test data processing device 300 of the SJT contextual child psychological assessment system further comprises:
and the obstacle association unit is used for constructing a psychological data report based on the distinguishing result and associating the psychological data report with different psychological obstacles.
Since the embodiments of the apparatus portion and the embodiments of the method portion correspond to each other, the embodiments of the apparatus portion are referred to the description of the embodiments of the method portion, and are not repeated herein.
The embodiment of the present application also provides a computer readable storage medium having a computer program stored thereon, which when executed can implement the steps provided in the above embodiment. The storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The embodiment of the application also provides a computer device, which can comprise a memory and a processor, wherein the memory stores a computer program, and the processor can realize the steps provided by the embodiment when calling the computer program in the memory. Of course, the computer device may also include various network interfaces, power supplies, and the like.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the application can be made without departing from the principles of the application and these modifications and adaptations are intended to be within the scope of the application as defined in the following claims.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (7)
1. A multi-dimensional test data processing method of an SJT situation type children psychological assessment system is characterized by comprising the following steps:
obtaining test results of a user on preset multidimensional test questions; the preset multidimensional test questions are multidimensional situation type animation test questions, and the situation type animation test questions comprise self concept test questions, emotion mastering test questions and cognitive development test questions;
statistically classifying the test results based on a potential class analysis method;
distinguishing the statistical classification result by using a bipartite mode to obtain a distinguishing result;
the statistical classification of the test results based on the potential category analysis method comprises the following steps:
constructing a potential category model for each situation type animation testing problem by using a potential category analysis method;
selecting an optimal category model from all potential category models by using a mode selection criterion;
statistically classifying the test results based on the optimal class model;
the method further comprises the steps of:
the self concept, emotion mastering and cognitive development are respectively used as potential variates, and the self concept testing questions, emotion mastering testing questions and cognitive development testing questions corresponding to the self concept, emotion mastering and cognitive development are respectively used as observation variates;
based on the observed variation item and the potential variation item, respectively constructing an analysis pattern diagram for self-concept, emotion mastering and cognitive development by utilizing the optimal category model;
and acquiring verification parameters of the potential variation and the observation variation according to the analysis mode diagram, and judging the performance of the optimal category model according to the verification parameters.
2. The method for processing multi-dimensional test data of SJT contextual child psychological assessment system according to claim 1, wherein said modulo criterion is a bayesian information criterion.
3. The method for processing multi-dimensional test data of SJT contextual child psychological assessment system according to claim 1, wherein said differentiating the statistical classification result by a binary method to obtain the differentiated result comprises:
acquiring a cut-off number according to the statistical classification result;
determining a first score above the cutoff score and a second score below the cutoff score based on the cutoff score;
and respectively transcoding the first fraction and the second fraction, and taking the transcoding result as a psychological test result.
4. The multi-dimensional test data processing method of the SJT contextual child psychological assessment system of claim 1, further comprising:
and constructing a psychological data report based on the distinguishing result, and associating the psychological data report with different psychological disorders.
5. A multi-dimensional test data processing device of an SJT contextual child psychological assessment system, comprising:
the result acquisition unit is used for acquiring test results of a user on a preset multi-dimensional test question; the preset multidimensional test questions are multidimensional situation type animation test questions, and the situation type animation test questions comprise self concept test questions, emotion mastering test questions and cognitive development test questions;
the statistical classification unit is used for statistically classifying the test results based on a potential category analysis method;
the result distinguishing unit is used for distinguishing the statistical classification result in a bisection mode and taking the distinguishing result as a psychological test result;
the statistical classification unit includes:
the model building unit is used for building a potential category model for each situation type animation testing problem through a potential category analysis method;
a model selection unit for selecting an optimal category model from all potential category models by using a mode selection criterion;
the model classification unit is used for carrying out statistical classification on the test results based on the optimal category model;
the apparatus further comprises:
a variation setting unit, configured to take a self concept, emotion grasp and cognitive development as potential variations, and take a self concept test question, an emotion grasp test question and a cognitive development test question, which correspond to the self concept, emotion grasp and cognitive development, as observation variations, respectively;
the pattern diagram construction unit is used for constructing an analysis pattern diagram for self-concept, emotion mastering and cognitive development respectively by utilizing the optimal category model based on the observation variation item and the potential variation item;
and the performance verification unit is used for acquiring verification parameters of the potential variation and the observation variation according to the analysis pattern diagram, and judging the performance of the optimal category model through the verification parameters.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a multi-dimensional test data processing method of the SJT contextual child psychological assessment system of any of claims 1 to 4 when the computer program is executed by the processor.
7. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the method for processing multi-dimensional test data of the SJT contextual child psychological assessment system according to any one of claims 1 to 4 is implemented.
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