Questionnaire data analysis method based on linear hidden variables

Abstract

The invention discloses a questionnaire data analysis method based on linear hidden variables. The method comprises the following steps: collecting filled questionnaires, and carrying out preprocessing and standardization processing on the questionnaires; constructing a measurement model, and obtaining a cluster of the observation variables and a skeleton diagram of hidden variables according to the observation variables after standardization processing; enumerating equivalence classes of the hidden variable skeleton graph, and carrying out tripartite constraint judgment; if the three-body constraint is violated, refusing to carry out connection; if the three-split constraint is met, taking each hidden variable as a root node, eliminating the influence from the root node on the rest hidden variables, and retaining corresponding equivalence classes; merging the reserved equivalence classes, outputting a causal structure diagram of the hidden variables according to a merging result, and obtaining a causal relationship among the hidden variables in the questionnaire. According to the method, the causal relationship among the hidden variables distributed in any form can be obtained, auxiliary analysis is carried out on the questionnaire, the analysis result is more accurate, and a correct decision can be made.

Description

technical field [0001] The present invention relates to the field of data processing and analysis, and more specifically, relates to a method for analyzing questionnaire data based on linear hidden variables. Background technique [0002] In econometrics, psychometrics and other fields, questionnaire is a commonly used method to study latent variables. For example, in market research, if you want to know the relationship between consumer behavior, attitudes and values, you often use a set of attitude scales for measurement. For example, in order to measure consumers' values, several questions can be designed for consumers to score. The collected data are only the measurement data of latent variables, and cannot directly reflect the relationship between latent variables. The typical analysis method of latent variables, that is, hidden variables, is factor analysis. Factor analysis assumes that the data follow a Gaussian distribution and studies the correlations in the data....

AI Technical Summary

Problems solved by technology

This method sets the displayed questions and hidden questions in advance, collects the answers, and performs data analysis. The analysis is still based on the correlation of the data. The causal relationship between the data cannot be obtained, and the accuracy of the data analysis of the questionnaire is not high.

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