臺灣博碩士論文加值系統

本研究目的在於發展青少年網路成癮危險因子量表。該量表含五個分量表與31個題目，分量表為：家庭關係挫折分量表、同儕關係挫折分量表、對線上人際互動具正向信念分量表、運用網路活動轉移負向情緒分量表，以及課業表現挫折分量表。

研究參與者為台中市751名國一至高三學生，參與者填寫網路成癮危險因子量表以及陳氏網路成癮量表。研究者透過Cronbach’s α係數考驗、因素分析、結構方程模式、Pearson積差相關分析、迴歸分析、ROC曲線分析、t 考驗、ANOVA，以及描述統計，分析量表的信效度，以及不同背景變項在量表的表現。

結果發現，青少年網路成癮危險因子量表具有良好的信度。於效度方面，本量表與效標關聯量表間具有中度相關，內部一致性尚佳，結構方程模式分析發現量表具有良好的模式適合度，但於內部結構尚需加強。本研究運用ROC曲線分析發現網路成癮風險組的切分點為36分，此分數乃供量表使用者參考，非用於分類學生的依據。

此外，青少年網路成癮危險因子量表和網路成癮量表具中度相關，羅吉斯迴歸發現此二量表具顯著的關係，勝率比為1.09。檢視參與者背景變項發現，男性、國三學生、年齡低於16歲、每天使用線上聊天超過1.77小時，以及每天玩網路遊戲超過1.23小時的學生，較易成為網路成癮的風險族群。教師與心理衛生專業人員可使用青少年網路成癮危險因子量表於網路成癮初期預防與介入，後續的研究建議亦於本文討論。

The discovery of risk factors for internet addiction can help educators and mental health professionals find predisposing conditions among youth and intervene early. This study aimed to develop Internet Addiction Risk Factor Scale for Adolescents (IARFS-A), a measure to assess internet addiction risk factors. Five subscales and a total of 31 items were generated. The five subscales included: Frustrations of family relationships subscale, frustrations of peer relationships subscale, positive belief of online social interaction subscale, transferring negative emotions by internet activities subscale, and frustrations of academic performance subscale.

A total of 751 students in 7th to 12th grades were recruited from schools in Taichung City to complete IARFS-A and the Chen Internet Addiction Scale (CIAS). Data were analyzed by Cronbach’s α, factor analysis, structural equation model (SEM), Pearson’s correlation, logistic regression, t-test, ANOVA, receiver operating characteristic (ROC) analysis, and descriptive statistics.

The findings showed that IARFS-A had good reliability (Cronbach’s alpha was.93). Regarding validity, IARFS-A had moderate correlation with criterion-related validity scales, acceptable internal consistency; SEM showed that IARFS-A had good model fit, but the internal structure needed to be improved. The risk group cut-off point was 36, which was just for educators and mental health professionals’ reference only, not for classifying students.

It was found that IARFS-A and CIAS had significantly moderate correlation (r=.64, p&;lt;.01), and logistic regression found that IARFS-A was significantly related to IA, odd ratio was 1.09 (p&;lt;.001). Participants who were males, 9th graders, under 16 years old, used online chatting over 1.77 hours/per day, and played online games over 1.23 hours/per day were more likely to be allocated to the internet addiction risk group. Educators and mental health professionals could use IARFS-A for internet addiction early prevention and intervention. Suggestions for application and future research were discussed.

TABLE OF CONTENTS
Chapter I Introduction 1
Research Background 1
Research Purposes 6
Definition of Terms 7

Chapter II Literature Review 9
Background Information about Internet Addiction 9
Risk Factors of Internet Addiction 15
Internet Addiction Related Mental Health Conditions 20

Chapter III Methods 23
Participants 23
Research Tools 23
Study Design 46
Data Analysis Plan 50

Chapter IV Results 55
Sample Characteristics 55
Results of Research AIM 1: Test the Reliability of IARFS-A 59
Results of Research AIM 2: Test the Validity of IARFS-A 65
Results of Research AIM 3: Characterize Relationships among IARFS-A, CIAS, and Usage of the Internet 72
Results of Research AIM 4: Compare IARFS-A Scores across Demographic Subgroups 76

Chapter V Discussion and Conclusions 81
Development of IARFS-A 81
Relationships among IARFS-A, CIAS, and Usage of the Internet 84
IARFS-A Scores across Demographic Subgroups 87
Application of IARFS-A 88
Limitations and Future Research Suggestions 89

References 91
Appendix A Research Ad (Chinese Version 101
Appendix B Informed Consent/Assent Form (Chinese Version 102
Appendix C Adolescent Life and Internet Usage Questionnaire (Chinese Version) 106
Appendix D Contact Information and Health Resources (Chinese Version) 116
Appendix E National Changhua University of Education IRB Approval Letter 118
Appendix F Approval Letters of Usage of Scales 119

LIST OF TABLES
Table 3.1 Item Analysis of Subscale A: Frustrations of Peer Relationship 26
Table 3.2 Item Analysis of Subscale B: Frustrations of Family Relationship 27
Table 3.3 Item Analysis of Subscale C: Biased Self Concepts 28
Table 3.4 Item Analysis of Subscale D: Transferring Negative Emotions by Internet Activities 30
Table 3.5 Item Analysis of Subscale E: Seeking Sensation Stimulus 31
Table 3.6 Item Analysis of Subscale F: Frustrations of Academic Performance 32
Table 3.7 Item Analysis of Subscale G: Positive Belief of Online Social Interaction 34
Table 3.8 Factor analysis for Principal Axis Factoring without Rotation of the IARFS-A Initial Version 36
Table 3.9 Factor Analysis for Principal Axis Factoring with Oblimin Rotation of the IARFS-A Initial Version 38
Table 3.10 Subscale 1: Positive Belief of Online Social Interaction 40
Table 3.11 Subscale 2: Frustrations of Family Relationships 41
Table 3.12 Subscale 3: Frustrations of Peer Relationships 41
Table 3.13 Subscale 4: Transferring Negative Emotions by Internet Activities 42
Table 3.14 Subscale 5: Frustrations of Academic Performance 42
Table 3.15 Assessment of Model Fit 52
Table 4.1 Sample Characteristics and Performance of Internet Addiction 56
Table 4.2 Usage of Internet by Gender and Grades 58
Table 4.3 Item Analysis of Subscale 1: Positive Belief of Online Social Interaction 59
Table 4.4 Item analysis of Subscale 2: Frustrations of Family Relationships 60
Table 4.5 Item analysis of Subscale 3: Frustrations of Peer Relationships 61
Table 4.6 Item Analysis of Subscale 4: Transferring Negative Emotions by Internet Activities 62
Table 4.7 Item Analysis of Subscale 5: Frustrations of Academic Performance 63
Table 4.8 Reliability of IARFS-A and Each Subscale……65
Table 4.9 Correlation between IARFS-A and Each Criterion-Related Validity Scale 67
Table 4.10 Results of Confirmatory Factor Analysis of IARFS-A 70
Table 4.11 Correlation between IARFS-A Total Scale and Each Subscale 72
Table 4.12 Correlation between IARFS-A and CIAS 73
Table 4.13 Univariate Logistic Regression Analysis for Association with Internet Addiction 74
Table 4.14 Correlation among IARFS-A, CIAS, and Usage of Internet 74
Table 4.15 t-test of Risk Group and Non-Risk Group on IARFS-A and CIAS 76
Table 4.16 Compare IARFS-A Scores across Demographic Subgroups for Total Participants 77
Table 4.17 Comparisons of Demographic Subgroups between Risk Group and Non-Risk Group 79

LIST OF FIGURES
Figure 4.1 IARFS-A Results of Confirmatory Factor Analysis and SEM Model 68

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