Graduation design projects every year are inseparable from user research methods, especially the use of market survey method based on questionnaire. Questionnaire surveys are mainly used to measure behavioral data and user attitude data that cannot be obtained from background big data, as well as based on user groups to validate and evaluate issue prioritization.<\/p>\n\n\n\n
The basic design method of the questionnaire can be summarized as one formula + two question types + three avoidances + four theories. “One formula” is to lock in the questionnaire outline (first purpose of questionnaire = dimension A + dimension B + dimension C). “Two question types” are yes-no questions and closed-questions. For yes-no questions, avoid using the word “no” and balance positivity and negativity. Closed-questions must follow exhaustiveness and mutual exclusivity, and retain exit items. Avoid extreme options, pay attention to the order of options, and put those that meet social expectations last. “Three avoidances” are to avoid tendentious descriptions, avoid vague wording, and avoid double questions. “Four theories” are that high-frequency behaviors use frequency questions, and low-frequency behaviors use possibility questions; high-frequency behaviors have a small time span, and low-frequency behaviors have a large time span; when the number of a certain behavior exceeds 5 times, it is an estimate instead of counting for the user; when asking about the frequency or number of times, use less fill-in-the-blanks and more distributed options commonly.<\/p>\n\n\n\n
The questionnaire outline of “First purpose of questionnaire = dimension A + dimension B + dimension C” is actually just a main structure of the questionnaire, which dismantles the problem in a multi-dimensional manner. A key point in questionnaire design is to break down the specific questions and options from dimensions based on the questionnaire outline. Each question needs to be written with a purpose and analysis method to ensure the rationality of the questionnaire design.<\/p>\n\n\n\n
After the first draft of the questionnaire is completed, it needs to be pre-tested, usually with a sample of 20-100 people. Using these sample data also requires testing the reliability and validity of the questionnaire. Specific testing methods can be obtained through literature search. For example, you can refer to the article “\u8c03\u67e5\u95ee\u5377\u7684\u4fe1\u5ea6\u5206\u6790\u548c\u6548\u5ea6\u5206\u6790<\/a>“.<\/p>\n\n\n\n Several points need to be emphasized here. The split-half reliability method is not suitable for measuring factual questionnaires. It is often used for reliability analysis of attitude and opinion questionnaires, such as Likert scale questions. Alpha reliability coefficient method is mainly used to evaluate the consistency of continuous variables and ordinal variables, such as Likert scale questions. The retest reliability method tests the same group of subjects twice, and then calculates the correlation coefficient of the scores obtained from the two tests, such as categorical variable questions. General fact-finding questions and dimensionless single questions are not subject to reliability analysis. The statistical analysis of content validity mainly uses the single item and sum correlation analysis method to obtain the evaluation results, that is, calculate the correlation coefficient between each item score and the total item score, and judge whether it is valid based on whether the correlation is significant; if there are negative questions in the scale, items should be reversely processed before calculating the total score. Structural validity is the most commonly used validity index, and the method used to analyze structural validity is factor analysis.<\/p>\n\n\n\n The SPSS statistical analysis process of questionnaire results includes four steps: defining variables, data entry, statistical analysis and result saving. There are as many variables as there are questions in a questionnaire, and the answer to each question is the value of the variable. Variables in multiple-choice questions are often defined using multiple dichotomies. The basic idea is to set each option of the question into a variable, and then split each option into two options, that is, check the item and not check the item. When entering data, one row represents one questionnaire, so if there are several questionnaires, there will be several rows of data. There are two types of statistical analysis: graphing analysis and numerical analysis. In SPSS, except for the survival curve graph used in survival analysis, which is integrated into the Analyze menu, other statistical drawing functions are placed in the Graph menu, and the numerical statistical analysis process is done all in the Analyze menu.<\/p>\n\n\n\n \u53c2\u8003\u8bd1\u6587<\/strong><\/strong><\/p>\n\n\n\n \u6bd5\u4e1a\u8bbe\u8ba1\u8bfe\u9898\u4e2d\u7684\u95ee\u5377\u8bbe\u8ba1\u4e0e<\/strong>SPSS<\/strong>\u7edf\u8ba1\u5206\u6790\u5f52\u7eb3<\/strong><\/strong><\/p>\n\n\n\n \u5173\u952e\u8bcd<\/strong>\uff1a\u6bd5\u4e1a\u8bbe\u8ba1\uff0c\u95ee\u5377\uff0cSPSS<\/p>\n\n\n\n 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