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Posts Tagged ‘Graduation Design’

Correlation and Nonparametric Statistics of Variables with Different Data Types in Graduation Design Project

2022年06月7日 留下评论

It’s the graduation season again, and it’s about time for the graduation defense this year. Graduation design projects this year involved eye movement research, “heads-down tribe” on campus, human-computer interface interaction design, and queuing theory application research. Except for queuing theory application research, which was selected by one of the students independently, other topics were given for reference this year. Among them, the human-computer interface interaction design subject was the first attempt to combine with the subject of the major of Information Management and Information System. Two students worked on the same project from different perspectives (Back-end database development, front-end interactive interface design). The specific projects are as follows:

Eye movement research project – eye movement research on visual contrast and intent affecting the attention of advertising keywords

Research project on “heads-down tribe” on campus——discomfort measurement on upper limb musculoskeletal system of college students with different levels of mobile phone use, research on influencing factors and prevention strategies of neck and shoulder pain of “heads-down tribe” on campus

Human-computer interface interaction design project——human-computer interface interaction design of c2c second-hand book information system on campus

Application research project of queuing theory——parking space matching and management charging standard of shopping mall underground parking lot based on queuing theory model

The research project of “heads-down tribe” on campus is my main research direction in recent years. There were three related topics here. Different research methods were used to research and design the discomfort of the upper limb musculoskeletal system of “heads-down tribe” on campus. Survey tools such as questionnaire and Likert scale were used for several times. Obviously, questionnaire and scale data are not continuous variables, and parametric statistical methods cannot be used directly. Even sEMG data and eye movement data collected during the ergonomic experiment needed to be tested for normality before using the parametric test method. Therefore, it is necessary to make a generalization of correlation analysis statistical methods and nonparametric statistical methods for different data types.

1. Data types of variables

The most common data classification method is to divide data according to the measurement level of data. Data can be divided into categorical variables, ordinal variables, equidistant variables and ratio variables. Equidistant and ratio variables are continuous variables, and categorical and ordinal variables are discrete variables. Equidistant variables have equal units but no absolute zero point, and can perform addition and subtraction operations, while cannot perform multiplication and division operations. Ratio variables have both equal units and absolute zero points, and can perform four arithmetic operations. Likert scale data are ordinal variables. Questionnaire data and independent variables in the experimental design are mostly categorical variables, and sEMG data and eye movement data are ratio variables. For ordinal variables such as Likert scale data, if they are identified as interval variables by the Mantel-Haenszel trend test, you can analyze interval ordinal variables as continuous variables.

2. Correlation analysis of variables with different data types

Pearson correlation is used to analyze the strength of linear association between two continuous variables, and the population from which the two columns of variables come must be normally or approximately normally distributed.

For correlation analysis between two ordinal variables, Spearman correlation is generally used to test the strength and direction of association with at least one ordinal variable, or two continuous variables but the population from which they are derived is not normal distribution or distribution is unknown.

Kendall’s tau-b correlation is a nonparametric analysis method used to test the strength and direction of association with at least one ordinal variable.

For the correlation analysis between two categorical variables, Chi-square test can be used to test their independence. This test can only analyze the statistical significance of the correlation and cannot reflect the strength of the association. It is often combined with Cramer’s V test to indicate the strength of the association.

For the correlation analysis between an ordinal variable and a continuous variable, the continuous variable is first tested as an ordinal variable, that is, to analyze the relationship between the two ordinal variables. Spearman correlation can be used.

For a detailed description of this part, please refer to the document as follows.

要做相关性分析,该如何选择正确的统计方法?

3. Normality test of sample data

One-sample K-S test can check whether the sample comes from a normally distributed population. Binomial method can test whether the actual distribution of the data in the binomial distribution conforms to a certain hypothesis, expectation, or specific form.

4. Nonparametric statistics of variables with different data types

Nonparametric tests with large samples are more reliable. In the case of a single sample, Chi-square test can be used to test the degree of cooperation to analyze whether the actual frequency of the variable value is consistent with the theoretical frequency.

To test whether the two independent samples come from the same population, or whether the data distribution of the two samples is the same, for the data that cannot meet the normal distribution condition, or two ordinal variables, Mann-Whitney U test needs to be used, which corresponds to independent sample t-test in parametric statistical method. It requires the independent variable to be a categorical variable with two levels, and the dependent variable to be an ordinal variable or continuous variable with at least an ordinal scale.

To test the significance between two related samples, it is usually applicable to two experimental design situations: repeated measures design and paired sample design. Four types of Wilcoxon signed-rank test, Sign test, McNemr test, and Marginal Homogeneity test can be used, corresponding to paired samples t-test and correlation coefficient significance test in parametric statistical method. Wilcoxon signed-rank test is the most widely used and is suitable for data with continuous distribution and symmetry. Sign test has a slightly lower statistical precision. McNemr test is only suitable for dichotomous correlated variables, and Marginal Homogeneity test is an extension of the McNemr test, which can test variables with multiple responses, but only for ordinal variables, and they are especially suitable for pretest-posttest experimental designs.

To test the significance among multiple independent samples, Kruskal-Wallis H test, Median test and Jonckheere-Terpstra test can be used, which correspond to the variance analysis of one-way completely randomized design in parametric statistical method. It requires the independent variable to be a categorical variable with more than two levels and the dependent variable be an ordinal variable or a continuous variable with at least an ordinal scale. Kruskal-Wallis H test corresponds directly to one-way ANOVA in parametric statistics and is frequently used. Median test is actually a contingency table analysis with low precision. Jonckheere-Terpstra test is similar to the Kruskal-Wallis H test, with higher precision when the grouping variable is ordinal.

To test the significance among multiple related samples, Friedman test, Cochrans Q test and Kendall W test can be used, which correspond to the variance analysis of randomized block design in parametric statistical method. Friedman test is an extension of Wilcoxon signed-rank test. Cochrans Q test is only applicable to several related dichotomous variables, which is an extension of McNemr test. Kendall W test is used to test whether the opinions of different evaluators are consistent. Both Friedman test and Cochrans Q test are applicable to repeated measures design and paired sample design. If there is a significant difference in the test results, further post-hoc tests are required, such as Wilcoxon signed-rank test.

For a detailed description of this part, please refer to the literature as follows.

丁国盛, 李涛编著. SPSS统计教程——从研究设计到数据分析. 北京: 机械工业出版社, 2014.

参考译文

毕业设计课题中的不同数据类型变量的相关与非参统计

关键词:毕业设计,数据类型,正态检验,非参统计

又到一年毕业季,马上就要进行今年的毕业设计答辩了。今年的毕业设计课题涉及眼动研究、校园低头族研究、人机界面交互设计和排队论应用研究,除排队论应用研究为学生自主选题外,其他课题均为今年给定的参考选题。其中人机界面交互设计课题为首次尝试与信息管理与信息系统专业交叉选题,课题研究对象选自信息管理与信息系统专业学生毕业设计,即两个专业的两名学生分别从不同角度(后台数据库开发、前台交互界面设计)对同一主题展开设计。具体选题如下:

眼动研究课题——视觉对比性与意图影响广告关键字注意力的眼动研究

校园低头族研究课题——手机不同使用程度的大学生上肢肌肉骨骼系统不适测评、校园低头族颈肩疼痛的影响因素及防范策略分析、不同情境下校园低头族手机使用的表面肌电研究

人机界面交互设计课题——高校c2c二手书信息系统人机界面交互设计

排队论应用研究课题——基于排队论模型的商场地下停车场车位匹配及管理收费标准

校园低头族研究课题是近年来本人的主要研究方向,这里有三个相关选题,运用了不同的研究方法对校园低头族上肢肌肉骨骼系统不适展开研究与设计,其中多次运用了问卷、里克特量表等调查工具。很显然问卷和量表数据都不是连续变量,都无法直接使用参数统计方法,即便是人因实验过程中收集的表面肌电数据和眼动数据,在运用参数检验方法前也需要进行正态检验。因此,有必要对不同数据类型的相关分析统计方法及非参统计方法做一次归纳。

1、变量的数据类型

最常见的数据分类方法是按照数据的测量水平来划分,可将数据区分为分类变量、顺序变量、等距变量和比率变量,其中等距变量和比率变量为连续变量,分类变量和顺序变量为离散变量。等距变量有相等单位但没有绝对零点,可进行加减运算,不能进行乘除运算;比率变量既有相等单位也有绝对零点,可以进行四则运算。里克特量表数据为顺序变量,问卷数据和实验设计中的自变量大部分为分类变量,表面肌电数据和眼动数据均为比率变量。对于里克特量表数据这一类的顺序变量,通过Mantel-Haenszel 趋势检验(根据研究者对顺序变量类别的赋值,判断两个顺序变量之间的线性趋势)认定为定距变量的话,也可以将定距顺序变量作为连续变量进行分析。

2、不同数据类型变量的相关分析

Pearson相关用于分析两个连续变量之间的线性关联强度,两列变量所来自的总体必须为正态或近似正态分布。

对于两个顺序变量之间的相关分析,一般采用Spearman相关(又称Spearman秩相关),用于检验至少有一个顺序变量的关联强度和方向,或者两个连续变量但所来自的总体非正态分布或分布未知。

Kendall’s tau-b相关用于检验至少有一个顺序变量关联强度和方向的非参分析方法,该检验与Spearman相关的应用范围基本一致,但更适用于存在多种关联的数据(如列联表)。

对于两个分类变量之间的相关分析,可采用卡方检验对它们进行独立性检验,该检验只能分析相关的统计学意义,不能反映关联强度,常联合Cramer’s V检验提示关联强度。

对于一个顺序变量和一个连续变量之间的相关分析,先将连续变量视为顺序变量进行检验,即分析两个顺序变量之间的关系,可采用Spearman相关。

关于这部分的详细说明可参考文献“要做相关性分析,该如何选择正确的统计方法?

3、样本数据的正态检验

单样本K-S检验可以检查样本是否来自正态分布总体,Binomial方法可以检验二项分布中数据的实际分布是否符合某一假设、预期或特定的形式。

4、不同数据类型变量的非参统计

大样本的非参检验更为可靠。单样本情形下,可采用卡方检验进行配合度检验,分析变量值的实际频数与理论频数是否一致。

检验两个独立样本是否来自同一总体,或者两个样本的数据分布是否相同,对于数据无法满足正态分布条件,或者两个顺序变量,需要采用Mann-Whitney U检验,对应于参数统计方法中的独立样本t检验,该检验要求自变量为两个水平的分类变量,因变量为至少达到顺序尺度的顺序变量或连续变量。

检验两个相关样本的差异显著性,通常适用于重复测量设计与配对样本设计两种实验设计情形,可以采用Wilcoxon符号秩检验、Sign检验、McNemr检验、Marginal Homogeneity检验4种,对应于参数统计方法中的配对样本t检验和相关系数显著性检验。Wilcoxon符号秩检验应用最广,适用于数据呈连续分布,有对称性。Sign检验统计精度略低。McNemr检验只适用于二分相关变量,Marginal Homogeneity检验是McNemr检验的扩展,可检验多重反应的变量,但仅限于顺序变量,它们特别适用于前测-后测的实验设计。

检验多个独立样本的差异显著性检验,可采用Kruskal-Wallis H检验、Median检验和Jonckheere-Terpstra检验,对应于参数统计方法中的单因素完全随机设计的方差分析,该检验要求自变量为两个以上水平的分类变量,因变量为至少达到顺序尺度的顺序变量或连续变量。Kruskal-Wallis H检验直接对应于参数统计中的单因素方差分析,使用率最高。Median检验事实上是列联表分析,精度较低。Jonckheere-Terpstra检验与Kruskal-Wallis H检验类似,当分组变量为顺序变量时精度更高。

检验多个相关样本的差异显著性,可采用Friedman检验、Cochrans Q检验和Kendall W检验,对应于参数统计方法中的随机区组设计的方差分析。Friedman检验是Wilcoxon符号秩检验的扩展,Cochrans Q检验只适用于几个相关的二分变量,是McNemr检验的扩展,Kendall W检验用于检验不同评价者的意见是否一致。Friedman检验和Cochrans Q检验都适用于重复测量设计与配对样本设计。如果检验结果发现存在显著性差异时,需要进一步进行事后检验,如采用Wilcoxon符号秩检验进行。

关于这部分的详细说明可参考文献“丁国盛, 李涛编著. SPSS统计教程——从研究设计到数据分析. 北京: 机械工业出版社, 2014。”

Start Preparing for Graduation Design Topics for The New Year

2020年12月31日 留下评论

At this time each year, we must start to prepare for graduation design topics for students who will graduate next year. With reference to previous years, students freely choose their graduation design instructors, and then further adjustments will be made later. Since the college began to implement the tutorial system, most students have chosen their own tutor when choosing an instructor under no special circumstances. Of course, there are exceptions. For example, my research direction is still unpopular in the department, so I will naturally choose fewer students.

At the same time, I also begin to prepare for topics on the graduation project. The topics can be prepared by the instructor, or students can make their own propositions, and the teacher can check and realize two-way topic selection. Also, in the previous blog post “Experience of Tutorial Students Participating in Research Training Program”, it was mentioned that in terms of topic selection for graduation design, considering that some students have gone through scientific research training before who have accumulated some relevant skills and experience, they continued to improve the original topic selection on the original basis which could reflect the typical research topic selection inheritance and continuity.

Based on the current research direction and subject content, I also initially made a list of topics.

1. Topics on virtual simulation for Ergonomic design

(1) Modeling and improved design of musical rocking chair based on Pro/E

(2) Modeling and simulation design of new foldable dining chair/bar chair based on Pro/E

(3) Other household products for Ergonomic design

2. Topics on mechanical structure design of experimental equipment for rehabilitation

(1) Research on methods and equipment for cervical spine stability evaluation

(2) Research on methods and equipment for lumbar stability evaluation

(3) Design of automatic detection device for classic falling ball experiment

(4) Design of experimental device for sudden imbalance of lower limbs

3. Topics on Ergonomic research direction of upper limb support

(1) Ergonomic research and development of hand support for laptop

(2) Ergonomic research and development of hand support for desktop

(3) Ergonomic research and development of hand support for tablet

4. Topics on the research direction of chronic neck pain and fatigue mechanism of “heads-down tribe” on campus

(1) Interview/scale survey on mobile phone use behavior and musculoskeletal system discomfort of “heads-down tribe” on campus

(2) Research on the observation method of mobile phone use behavior of “heads-down tribe” on campus in different situations

(3) Surface electromyography experiments and upper limb musculoskeletal system discomfort scale evaluation of mobile phone use and operation of “heads-down tribe” on campus in different situations

5. Research on Human-Machine Interface satisfaction and interaction design and interface development

(1) The impact of advertising information and advertising presentation methods on WeChat/QQ/other APP advertising attention

(2) Research on eye movement of visual contrast and intent affecting the attention of advertising keywords

(3) The influence of visual guidance on the learning performance of multimedia teaching materials

(4) Research on eye movement of the homepage of the digital library on the visual attractiveness of young people

(5) Innovative design of E-commerce platform/mobile payment man-machine interface

(6) User interface design based on usability engineering

(7) Context-based student evaluation system interface development and design

Graduation Design Comment Template Backup

2020年06月11日 留下评论

前几天刚刚完成了2016级毕业设计答辩,正在写相关的评语,现将几类评语的模板加以备份,今后用起来也方便。

指导评语模板:

优秀(90-100分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计选题符合专业培养目标,能够达到综合训练目标,题目有一定难度,工作量大,选题具有较高的学术研究参考价值;格式完全符合规范要求,论文叙述恰当和清晰,具有一定的独立见解;该设计方案能独立完成,思路正确,单因素的实验设计方法合理,数据分析和处理方法正确;论文内容完整,层次结构安排科学合理,主要观点突出,逻辑关系清晰,图纸质量较好,符合规范要求;计算机应用能力强,查阅文献资料能力强,能全面收集(论文所属领域)的资料,参考了丰富的文献资料,时效性较强。

该生在毕业设计期间能很好地遵守纪律,工作责任心好,态度端正,学习主动性强。

良好(80-89分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计方案思路基本正确,立论正确合理,数据收集方法可靠,基本可以独立完成;论文叙述恰当和清晰,具有一定的独立见解;论文、图纸质量较好,基本符合规范要求;实验设计/数据收集/等方法较为合理,数据分析和处理方法基本正确,应用文献资料解决实际问题的能力较好;计算机应用能力较强,文献检索和引用能力较好。

该生在毕业设计期间能很好地遵守纪律,工作责任心较好,态度端正,学习主动性较好。

中等(70-79分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计方案思路基本正确,立论正确合理,数据收集方法可靠,基本可以独立完成;论文叙述恰当和清晰,具有一定的独立见解;论文、图纸质量一般,基本符合规范要求;但实验设计/数据收集/等方法合理性值得商榷,数据分析和处理方法基本正确,应用文献资料解决实际问题的能力一般;计算机应用能力一般,文献检索和引用能力一般。

该生在毕业设计期间能较好地遵守纪律,工作责任心较好,态度较端正,学习主动性一般。

及格(60-69分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计的方案选择尚能独立完成,立论基本正确;分析基本合理,实验设计方法/采集的数据/等尚可,计算存在一些错误;有一些独立见解,但实用价值不高。说明书、论文基本正确,图纸、图表尚规范工整。数据处理基本正确,应用文献资料解决实际问题,针对性不足。计算机应用能力较差,引用国内外期刊杂志一般。

该生在毕业设计期间能遵守纪律,认真完成任务,工作责任心,主动性相对较一般。

评阅评语模板:

优秀(90-100分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计选题符合专业培养目标,能够达到综合训练目标,选题具有较高的学术研究参考价值;格式完全符合规范要求,论文叙述恰当和清晰,具有一定的独立见解;该设计方案能独立完成,思路正确,论文内容完整,层次结构安排科学合理,主要观点突出,逻辑关系清晰,图纸质量较好,符合规范要求;计算机应用能力强,查阅文献资料能力强。

建议该毕业设计成绩为优秀。

良好(80-89分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计方案思路基本正确,立论正确合理,数据收集方法可靠,基本可以独立完成;论文叙述恰当和清晰,具有一定的独立见解;论文、图纸质量较好,基本符合规范要求;实验设计/数据收集/等方法较为合理,数据分析和处理方法基本正确,应用文献资料解决实际问题的能力较好;计算机应用能力较强,文献检索和引用能力较好。

建议该毕业设计成绩为良好。

中等(70-79分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计方案思路基本正确,立论正确合理,数据收集方法可靠,基本可以独立完成;论文叙述恰当和清晰,具有一定的独立见解;论文、图纸质量一般,基本符合规范要求;实验设计/数据收集/等方法基本合理,数据分析和处理方法基本正确,应用文献资料解决实际问题的能力一般;计算机应用能力一般,文献检索和引用能力一般。

建议该毕业设计成绩为中等。

及格(60-69分)

该设计(后面摘抄论文摘要的方法、结果与结论部分文字)。

该设计主要部分完成了毕业设计任务书规定的内容,分析尚清楚,计算尚准确,计算机和外语运用能力一般,缺乏个人见解,各类资料错误较多。

建议该毕业设计成绩为及格。

答辩过程中主提问教师评价意见模板:

优秀(90-100分)

92分:该生在答辩过程中态度认真谦虚,尊重老师;答辩思路清晰、思维敏捷、语言表达准确、叙述论文全面、重点突出,回答问题基本正确;答辩时间符合要求。

良好(80-89分)

80分:该生在答辩过程中态度较为认真;答辩的思路比较清晰,语言流畅,比较全面的论述了文章内容,回答问题基本正确,答辩时间符合论文答辩要求。

中等(70-79分)

77分:该生在答辩过程中态度较为认真;答辩的思路比较清晰,语言流畅,比较全面的论述了文章内容,回答问题基本正确,答辩时间符合论文答辩要求。

73分:该生在答辩中态度较为认真;答辩思路比较清晰,但表达能力欠缺;阐述文章内容比较全面,但存在计算错误,重点不够突出;论文回答问题在老师的提示下能基本无误;时间符合答辩要求。

及格(60-69分)

66分:该生毕业设计的结论基本无误,但个人见解不突出;在答辩中态度较为认真;但思路不够清晰,基本能够正确的阐述论文内容,但表达无重点,定性研究内容过多;勉强能正确回答老师提出的问题;时间基本符合答辩要求。

66分:该生毕业设计的结论基本无误,但个人见解不突出;在答辩中态度较为认真;但思路不够清晰,基本能够正确的阐述论文内容,但表达无重点,论文部分章节存在抄袭内容;勉强能正确回答老师提出的问题;时间基本符合答辩要求。

66分:该生毕业设计的结论基本无误,但个人见解不突出;在答辩中态度较为认真;但思路不清晰,基本能够正确的阐述论文内容,但表达无重点,论文的部分计算错误;勉强能正确回答老师提出的问题;时间基本符合答辩要求。