﻿ 当代大学生对党的十九大关注度影响因素分析——基于南京部分高校调查数据 Research on College Students’ Concern about the 19thNational People’s Congress—Based on Some Universities in Nanjing City, Jiangsu Province

Statistics and Application
Vol. 08  No. 04 ( 2019 ), Article ID: 31706 , 11 pages
10.12677/SA.2019.84075

Research on College Students’ Concern about the 19th National People’s Congress

—Based on Some Universities in Nanjing City, Jiangsu Province

Yan He, Yang Li, Shan Rong, Conghui Zhang

Nanjing Audit University, Nanjing Jiangsu

Received: Jul. 25th, 2019; accepted: Aug. 7th, 2019; published: Aug. 14th, 2019

ABSTRACT

The 19th National Congress of the Communist Party of China is an important meeting that has attracted much attention at home and abroad. College students are the future of a country, and contemporary college students' attention to the 19th National People’s Congress is a reflection of students’ political accomplishments. Analyzing the influence factors of contemporary college students on the attention of the 19th National People’s Congress is conducive to the understanding of the political literacy of students by society and schools. It is beneficial to draw a feasible solution for improving students’ attention to the 19th National People’s Congress. In this paper, questionnaire data are systematically analyzed by SPSS through regression and then the proposed factors in affecting attention of the 19th National People’s Congress are evaluated.

Keywords:The 19th National People’s Congress, Questionnaire, Regression Analysis

——基于南京部分高校调查数据

Copyright © 2019 by author(s) and Hans Publishers Inc.

1. 问题提出

2. 研究设计与假设

2.1. 数据与样本

${n}_{0}=\frac{{\left({z}_{\alpha /2}\right)}^{2}\pi \left(1-\pi \right)}{{E}^{2}},$

${n}_{1}=\frac{{n}_{0}}{1+\frac{{n}_{0}}{N}},$

${n}_{0}=\frac{{\left({z}_{\alpha /2}\right)}^{2}\pi \left(1-\pi \right)}{{E}^{2}}=\frac{{2.58}^{2}*0.5*0.5}{{0.05}^{2}}\approx 666,$

${n}_{1}=\frac{{n}_{0}}{1+\frac{{n}_{0}}{N}}=\frac{666}{1+\frac{666}{53000}}\approx 658.$

${n}_{0}=\frac{{\left({z}_{\alpha /2}\right)}^{2}{\sigma }^{2}}{{E}^{2}},$

${n}_{1}=\frac{{n}_{0}}{1+\frac{{n}_{0}}{N}},$

${n}_{0}=\frac{{\left({z}_{\alpha /2}\right)}^{2}{\sigma }^{2}}{{E}^{2}}=\frac{{2.58}^{2}*{0.402}^{2}}{{0.1}^{2}}\approx 108,$

${n}_{1}=\frac{{n}_{0}}{1+\frac{{n}_{0}}{N}}=\frac{108}{1+\frac{108}{53000}}\approx 108.$

2.2. 分析方法与步骤

1) 自我评估结果分析：首先，将自我评估分类结果与待检验因素进行卡方检验，检测二者是否独立。其次，采用向后法–似然比检验(LR)筛选变量以检验变量是否真实独立，进行二分类logistic回归确定各因素的影响程度和方向。最后，分析回归结果。

2) 客观实测结果分析：首先，将实测结果与待检验因素进行卡方检验，检测二者是否独立。其次，对待检验因素和分类结果进行平行线检验，然后进行有序多分类logistic回归确定各因素的影响程度和方向。最后，分析回归结果。

3) 比较二者结果差异进行综合分析，得出结论。

H0：该因素与分类结果独立；H1：该因素与分类结果不独立。

H0：该因素对十九大关注度有影响；H1：该因素对十九大关注度没有影响。

3. 实证分析

3.1. 自我评估结果分析

1) 自我评估的结果分布

Table 1. Self-assessment results distribution

2) 自我评估结果与各因素的卡方检验

Table 2. Chi-square test of interaction between self-assessment and school

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为99.00。

Table 3. Chi-square test of interaction between self-assessment and gender

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为136.97；b. 仅针对2 × 2表进行计算。

Table 4. Chi-square test of interaction between self-assessment and grade

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为 93.24；b. 仅针对2 × 2表进行计算。

Table 5. Chi-square test of interaction between self-assessment and political appearance

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为12.50。

Table 6. Chi-square test of interaction between self-assessment and major

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为 8.71；b. 8个单元格(50.0%)的期望计数小于5，最小期望计数为1.83；c. 1个单元格(6.3%)的期望计数小于5，最小期望计数为 4.98；d. 6个单元格(42.9%)的期望计数小于5，最小期望计数为1.27。注：剔除了被调查人数较少的专业，避免对卡方检验造成影响。

3) 二分类logistic回归

Table 7. Variables screened by backward method-likelihood ratio test

a. 在步骤1输入的变量：学校，性别，年级，政治面貌。

3.2. 实测结果分析

1) 实测结果的分布情况

Figure 1. Distribution of testing results of the 19th national congress

2) 实测结果分类与各因素的卡方检验

Table 8. Chi-square test of interaction between testing results and school

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为25.52。

Table 9. Chi-square test of interaction between testing results and gender

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为35.31。

Table 10. Chi-square test of interaction between testing results and grade

a. 0个单元格(0.0%)的期望计数小于5，最小期望计数为6.07。

Table 11. Chi-square test of interaction between testing results and political appearance

a. 5个单元格(25.0%)的期望计数小于5，最小期望计数为3.22。

Table 12. Chi-square test of interaction between testing results and major

a. 11个单元格(27.5%)的期望计数小于5，最小期望计数为2.20；b. 33个单元格(82.5%)的期望计数小于5，最小期望计数为0.26；c. 23个单元格(57.5%)的期望计数小于5，最小期望计数为 1.15；d. 21个单元格(60.0%)的期望计数小于5，最小期望计数为0.25。注：剔除了被调查人数较少的专业，避免对卡方检验造成影响。

3) 有序多分类logistic回归

Table 13. Ordered multi-class logistic regression of testing results

a. 此参数冗余，因此设置为零。

3.3. 对比分析

4. 结论与讨论

Research on College Students’ Concern about the 19thNational People’s Congress—Based on Some Universities in Nanjing City, Jiangsu Province[J]. 统计学与应用, 2019, 08(04): 664-674. https://doi.org/10.12677/SA.2019.84075

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