Zexi Su

doi:10.4040/jkan.25089

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Research Paper
Validity and reliability of the Security Neglect Subscale of the Child Neglect Scale in vulnerable Chinese children: a methodological study: Zexi Su; DOI: https://doi.org/10.4040/jkan.25089
Published online: November 27, 2025

School of Social Development, Shandong Women’s University, Jinan, China

Corresponding author: Zexi Su School of Social Development, Shandong Women’s University, Jinan Shandong 250030, China E-mail: 18733909575@163.com

• Received: June 26, 2025 • Revised: October 5, 2025 • Accepted: October 6, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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Abstract
Introduction
Methods
Results
Discussion
Conclusion
Article Information
References

Abstract

Purpose
Security neglect is common among vulnerable children. The Child Neglect Scale (CNS) is widely used to screen children for neglect. However, little is known about the accuracy of the Security Neglect Subscale when administered in isolation. This study aimed to examine the reliability and validity of the Security Neglect Subscale of the CNS among vulnerable children in China.
Methods
Cluster sampling was used, and 242 vulnerable children participated in the study. Data were analyzed using IBM SPSS ver. 28.0 and Amos ver. 28.0, and the test construct validity of the CNS Security Neglect Subscale was analyzed through confirmatory factor analysis. In addition, convergent and discriminant validity, as well as reliability, were evaluated.
Results
The construct validity of the nine-item CNS Security Neglect Subscale was confirmed by a two-factor structure. The modified model fit the data well, as shown by a normed chi-square of 2.48, a comparative fit index of .97, a Tucker-Lewis index of .96, and a root mean square error of approximation of .08. The model had acceptable convergent and discriminant validity for each structure. The Cronbach’s α coefficient was .87 overall, and values for the two factors ranged from .78 to .93.
Conclusion
The findings of this study support the satisfactory psychometric properties of the CNS Security Neglect Subscale, indicating its utility in evaluating security neglect in vulnerable children in China.
Key words: Child abuse; Reproducibility of results; Validation study

Introduction

Neglect is frequently characterized as the failure to meet essential needs [1]. Childhood neglect is prevalent in all societies as an adverse childhood experience. Previously, the concept of child neglect has lacked a uniform operational definition. The definition of neglect may be related to the type of neglect, severity, duration, or even to the age of the child [2]. In recent years, several researchers have suggested that defining the concept of child neglect should be informed by child development theory. Child development theory suggests that children have specific developmental needs at different stages of growth, and that if these needs are not met, they will be hindered from adapting smoothly across developmental stages. Therefore, in this study, neglect is considered to be when a child’s fundamental physical or psychological needs are not met, resulting in a risk of harm or less-than-optimal development [3]. Child neglect exerts profound and enduring adverse effects on minors’ developmental outcomes [4]. In previous research, neglect and abuse often appeared together, or neglect was used as a category of abuse [5]. Today, however, many researchers believe neglect should be distinguished from abuse and should be given adequate attention [6]. The consequences of neglect are more insidious than those of abuse and are a more pervasive form of victimization [7]. Childhood neglect exposure elevates lifelong vulnerability to multisystemic impairments spanning physical health, psychological functioning, and social adaptation [8].

Safety is a crucial aspect of children’s needs [9]. Children are more vulnerable to environmental risks than adults. Creating a safe physical environment is crucial for children. It affects their development over time and protects them from the immediate risk of accidental injury. Accidental injury is the leading cause of disability and death in children. Most of these deaths are preventable [3]. Children may be unable to recognize risks or make informed choices to protect their health, unlike adults [10]. The World Health Organization states that environmental hazards can exist wherever children live, play and learn [11]. Moreover, vulnerable children exposed to multiple suboptimal physical and social environments may face more significant safety threats [12]. The United Nations Convention on the Rights of the Child states that children must have the opportunity to grow and develop in the healthiest and safest environment possible (Article 6) and that children should be adequately protected by their guardians. Children exposed to the best possible physical and social environment from an early age have a better chance of growing up healthy and happy. Adverse early childhood experiences are significantly associated with poor health outcomes, lower educational achievement, economic dependence, heightened risks of violence and criminal behavior, as well as substance abuse and depression [13]. Moreover, these can increase the burden and costs to society, including the health system [13]. Because of the implications of security neglect for areas such as child health and public health, nurses must conceptualize and measure security neglect when counselling children, especially vulnerable children.

While high-quality, evidence-based assessments are fundamental to good practice and crucial for measuring security neglect, few validated tools currently exist specifically for this purpose [14]. A global systematic review conducted by the UK’s National Institute for Health and Care Excellence (NICE) of guidance on child abuse and neglect found no high-quality evidence to demonstrate the predictive validity of existing assessment tools for identifying neglect [15]. A study found that only the Child Neglect Index (CNI) and a modified version of the Maltreatment Classification System (MMCS) met the inclusion criteria after a systematic search for child neglect measurement tools [9]. The CNI was found to be short and easy to use, but did not cover all situations. The MMCS was also noted to have flaws, and some researchers felt that it was time-consuming and difficult to administer practically, and not worth testing [9].

Chinese scholars have created several tools to assess child neglect. The Childhood Trauma Questionnaire (CTQ), developed by Bernstein and Fink [16] in 1998, is one of the most recognized instruments in the world for measuring childhood maltreatment. In 2004, Chinese scholars revised the Chinese version of the CTQ to measure child abuse and neglect in China, including emotional neglect and physical neglect [17]. Although the Chinese version of the CTQ showed good reliability and validity [18], it only has 10 items to assess neglect, which may not be able to describe the complete picture of child security neglect. Yang [6] developed the Chinese Cultural Context-Based Child Neglect Scale (CNS), which consists of four subscales: security neglect, physical neglect, communication neglect, and emotional neglect. Security neglect has been defined as ignoring safety hazards in a child’s development and living environment. The CNS has been used to evaluate child neglect in several Chinese studies and has good reliability and validity [19]. The CNS has a total of 38 items and is used to assess neglect experienced by children. The full version of the CNS is time-consuming to examine. The Security Neglect subscale can help caregivers and researchers closely monitor and quickly track child security neglect. Researchers have also found a two-factor structure or dimensionality characteristic of the CNS Security Neglect Subscale [6]. However, the psychometric properties of the CNS have not been validated for use in a group of vulnerable children. Some items in the original scale must be revised to accommodate vulnerable children. In addition, the reliability and validity of the Secure Neglect Subscale when administered alone have not been examined.

Therefore, this study aimed to test the validity and reliability of the CNS Security Neglect Subscale among vulnerable children. The results will provide evidence for future research to measure and understand the status and future development of child security neglect among vulnerable children.

Methods

1. Study design

This study examined the validity and reliability of the Child Security Neglect Subscale in the psychometric measurement to determine whether the scale can be used in research with vulnerable children.

2. Setting and sample

The data of this study were collected from July to October 2024. There are five cooperating counties in Shandong Province, China, for the Vulnerable Child Assessment Programme from which we randomly chose, by cluster sampling, a particular county. This study surveyed all vulnerable children registered with the government in this county. We collected survey responses that included 253 vulnerable children.

Inclusion criteria for all participants were (1) age 5–18 years; (2) normal cognitive development and ability to understand Chinese; and (3) consent to participate in the study. Following screening, 11 ineligible participants were excluded (six did not complete the survey, and five had intellectual disabilities that prevented them from answering the questionnaire). The group that withdrew from the study was not significantly different from the current study sample in terms of age and gender. Ultimately, data from a total of 242 participants were included in the statistical analyses. Evidence from cognitive science indicates that children can fulfil the requirements for answering questionnaires [20]. At the age of 5, children are already able to describe internal mental states, including perceptual, emotional, cognitive, and physiological states [21]. This critical stage is the ideal time for early intervention services to prevent subsequent developmental problems. Therefore, the criteria for sample inclusion in this study were set at 5–18 years of age. If any participant was too young to fully understand the questions, the interviewers would proactively interpret the questions for them. According to the recommendations by Costello and Osborne [22] for determining sample size for confirmatory factor analysis, the item ratio of a 20:1 sample has higher accuracy, and our sample size met this criterion.

3. Instruments

The CNS is a retrospective self-report scale to evaluate neglect of children. It includes emotional neglect, security neglect, physical neglect, and communication neglect. Security neglect is defined as the neglect of safety hazards in the environment in which a child is growing up and living, thereby placing the child at risk of health and life hazards. It is scored on a 4-point Likert scale (from ‘1=none’ to ‘4=always’), with higher scores indicating more severe neglect. In the original study, the scale had an overall Cronbach’s α coefficient of .85, a split-half coefficient of .81, and a retest coefficient of .89. In previous studies, the CNS had good reliability and validity in research samples among Chinese children [6].

The Security Neglect Subscale, which is the focus of this study, was analyzed to assess the presence of some safety hazards in the child’s growing environment due to the guardian’s negligence, which may lead to a risky situation for the child. There were nine items containing two factors (e.g., Parents told me to be careful when crossing the street; Parents told me how to do when I am in danger locked me up alone in the house; ignored by my parents when telling them that my peers were bullying me). Some of the items were revised to accommodate groups of vulnerable children (by replacing “parents” with “guardians” in items 1, 2, 3, 4, 6, and 8). Items 1, 2, 3, 4, and 6 are reverse scored.

4. Data collection

Firstly, the researcher contacted the local government and established a cooperative relationship. After fully explaining the purpose of the study, the questionnaire was submitted to the local government, and a trial survey was conducted. Thirty vulnerable children were randomly selected as a sample, and the researcher further revised and improved the questionnaire after analysis, according to which a specific implementation plan was formulated. It then entered the formal survey phase. Data were collected through face-to-face interviews. Before the interviewers collected the data, the researcher trained them in uniformity and standard instructions during the interview process, controlling the interview time and data checking and entry after the information collection was completed.

5. Data analysis

IBM SPSS ver. 28.0 and IBM SPSS AMOS ver. 28.0 statistical software (IBM Corp.) were used to analyze the data. Firstly, item analyses were performed. Normality was evaluated using skewness and kurtosis coefficients, with absolute values below 3 for skewness and below 10 for kurtosis serving as the criteria for all variables [23]. Item contribution was assessed by analyzing the item-total correlation (≥.30) [24].

Confirmatory factor analysis (CFA) assessed the scale’s construct validity. Kaiser-Meyer-Olkin (KMO) and Bartlett’s test were used to assess the suitability of the data for factor analysis [23]. CFA is suitable for applying instruments with a defined factor structure based on theoretical foundations to examine new populations. As recommended by Hu and Bentler [25], CFA uses normed chi-square (χ²/degrees of freedom [df]), comparative fit index (CFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA) to assess model fit. Acceptable fit was indicated by χ²/df ≤3.0, RMSEA ≤.08, GFI ≥.90, NFI ≥.90, CFI ≥.90, and TLI ≥.90 [26].

The items’ convergent validity was assessed using average variance extracted (AVE) and composite reliability (CR). The criteria for satisfactory convergent validity were set at AVE ≥.50 and CR ≥.70 [27]. Item discriminant validity was determined by comparing the AVE values to the squared values of the correlation coefficients for each subdomain of the Security Neglect Subscale [28].

Cronbach’s α coefficient assessed the tool’s internal consistency reliability, with coefficients >.70 considered sufficient [29]. In this study, p<.05 indicates statistical significance.

6. Ethical considerations

This study has been authorized by the original developers of the CNS tool. Ethical approval for this study was granted by the Institutional Review Board at Shandong Women’s University (approval no., sdwu-20240506-01). This study obtained cooperation with the local government through which the survey was conducted, and the final data was obtained. The researcher submitted the questionnaire to the government staff before data collection. After reviewing the questionnaire and receiving approval from the researcher’s organization and the government where the survey was conducted, the questionnaire and procedures of this study were safe for the participants. Written informed consent was obtained from participants and their guardians.

Results

1. Participants’ characteristics

The sample consisted of 242 vulnerable children with a mean age of 12.5 years (standard deviation=3.24). Of these participants, 134 (55.4%) were boys and 108 (44.6%) were girls. Regarding educational level, the majority of participants, 43%, were enrolled in primary school and below, followed by junior high school (33.1%) and senior high school (18.6%). Among the vulnerable children, 30 (12.4%) were from intact families, 114 (47.1%) were from single-parent families, and 49 (20.2%) were orphans (Table 1).

2. Item analysis

The skewness of the items ranged from –2.87 to –1.42, and the kurtosis ranged from 1.23 to 9.30. The absolute value of the skewness was less than 3, the absolute value of the kurtosis was less than 10, and the items met normality. The corrected item-total correlation coefficients ranged from .41 to .74, so no items were deleted (Table 2).

3. Validity

1) Construct validity

The KMO was .85, and Bartlett’s sphericity test was significant (χ²=1,330.02, p<.001), indicating suitability for factor analysis. The CFA model of the measurements was fitted to the data (Table 3). The Security Neglect Subscale is composed of nine items in two dimensions. The results of the CFA were as follows: χ²/df=3.42, NFI=.93, TLI=.93, CFI=.95, and RMSEA=.10. However, among the model fit indices, the RMSEA values were not met. To improve the model fit, this study confirmed the error term’s modification index and set the covariance between items 1 and 2, 2 and 3. As a result, the CFA model fitted the data very well: χ²/df=2.48, NFI=.96, TLI=.96, CFI=.97, and RMSEA=.08.

2) Convergent validity

This study assessed the convergent validity of items in the Security Neglect Subscale (Table 4). The standardized factor loadings of the modified model ranged from .60 to .91. Additionally, the AVE values for each dimension were .73 and .48, respectively, and the CR values were .79 and .93, respectively. The AVE value for the security neglect dimension was slightly less than .50. Although the AVE for the security neglect dimension fell marginally below the conventional threshold of .50, a more conservative assessment of the scale’s internal structure supported the adequacy of convergent validity when CR exceeded .60 [30].

When the two factors’ AVE values were compared to the squared correlation coefficients (r²=.15), the AVE values were greater. These findings provide evidence that the subscales have good discriminant validity.

3) Reliability

Internal consistency reliability was verified by calculating Cronbach’s α coefficient. The Cronbach’s α coefficient for the nine items of the Security Neglect Subscale was .87, and for the two factors, it was .78 and .93, respectively.

Discussion

This study validated the validity and reliability of the CNS Security Neglect Subscale in vulnerable children. The original scale was revised to accommodate vulnerable children. The psychometric properties were assessed in 242 children aged 5–18. The results showed that the 9-item CNS Security Neglect Subscale had good validity and reliability in assessing the security neglect status of vulnerable children in China. The use of this instrument may contribute to a full understanding of vulnerable child security neglect, leading to positive outcomes for children’s subsequent development. The CNS Security Neglect subscale demonstrates significant advantages over existing measurement tools. Through self-reports from a child’s perspective, groups of vulnerable children experiencing security neglect are quickly identified using concise question items. This facilitates timely intervention or referral for services.

In this study, the wording was carefully considered to ensure the appropriateness of the tool in order to accommodate the vulnerable child population. For example, the key consideration was that a significant proportion of vulnerable children have incomplete family structures, so we changed the subject of neglect in the original scale from parents to guardians. The CFA was used in this study to confirm the structural validity of the Security Neglect Subscale. The two-factor structure remained stable in the group of vulnerable children, and the fit was fundamentally satisfactory. The present study model fit was improved by allowing correlated measurement errors. This strategy has been used in many studies [31,32]. Model fit indices are reported both prior to and following correlated error incorporation to clarify inter-model differences. The factor loadings for all items in this study ranged between .60 and .91. These findings are consistent with the original study of this tool [6].

AVE and CR were additionally examined to verify items’ accurate and consistent representation of their target constructs. While security neglect’s AVE fell marginally below the recommended threshold, satisfactory CR values confirmed adequate convergent validity for its items. Discriminant validity was also tested through CFA methods and supported in the current study.

The internal consistency of the Security Neglect Subscale was assessed using the Cronbach’s α coefficient. The subscale and both factors in the current study showed good internal consistency (Cronbach’s α coefficients of .78 and .93). Item-total correlations were also calculated to determine the relationship between items and scale scores. It is recommended that item-total correlations for items should be above .20 [33]. In the present study, item-total correlations ranged between .41 and .74, which indicates sufficient internal consistency of the scale.

Although hidden, child neglect is closely linked to children’s functional development [3]. This study clarifies whether vulnerable children are neglected and to what extent during interventions. Such insights help monitor guardianship status and guide efforts to improve guardians’ safety literacy [34], reducing unintentional injuries [35] and unlawful abuse [35,37]. Nurses, as the largest healthcare professional group within the field of child and family care, offer fresh perspectives for advancing public health solutions to child neglect [38]. Universal screening through comprehensive health services (i.e., screening all families at primary healthcare facilities) can eliminate the stigma associated with selective screening [39], reduce the likelihood of overlooking high-risk families, and facilitate early identification of patient needs using concise psychosocial tools. When risks or adverse effects are identified, it is crucial to take decisive action to protect children. This may involve follow-up when situations are ambiguous and collaborating with other professionals, such as social workers, when additional family support is deemed necessary. If vulnerable children reach a critical level of harm, nurses may report cases of neglect to child protection services. Nurses play a pivotal role in public health responses, and this study provides healthcare practitioners with a concise screening tool to support their professional practice.

There are some limitations to this study. Firstly, this study did not validate the retest reliability of the tool. We could not find participants again because the data came from a collaboration with the government to collect data. Future studies should validate the instrument’s consistency by assessing its retest reliability. Second, this study was conducted in a county in Shandong Province, China. However, there is a great deal of variation between regions in China and a large population. Whether the findings of this study can be generalized to vulnerable children in other regions needs to be validated in future studies. Third, the final model incorporated correlated error terms. Future studies should streamline items with content redundancy.

Conclusion

The present study confirms that the CNS Security Neglect Subscale is a valid and reliable instrument for assessing the security neglect status of vulnerable children. Our findings confirm two factors in the CNS Security Neglect Subscale. Researchers can adopt intervention strategies according to the level of security neglect. In the future, it is necessary to validate the reliability and validity of the Security Neglect Subscale with larger sample sizes and to broaden our understanding of the impact of security neglect on vulnerable child health outcomes.

Article Information

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Acknowledgements

We are grateful to the vulnerable children who participated in this study, and the team that assisted in data collection.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Data Sharing Statement

Please contact the corresponding author for data availability.

Author Contributions

Zexi Su conceived and designed the study, analyzed the data, and wrote the entire manuscript.

Table 1.

Descriptive characteristics of participants (N=242)

Characteristic	Category	N (%)
Gender	Women	108 (44.6)
	Men	134 (55.4)
Age (yr)	5–12	113 (46.7)
	13–18	129 (53.3)
Education	Primary school or less	104 (43.0)
	Junior school	80 (33.1)
	High school	45 (18.6)
	Dropout	2 (0.8)
	Missing	11 (4.5)
Family type	Complete family	30 (12.4)
	Orphan	49 (20.2)
	Single-parent family	114 (47.1)
	Step-family	3 (1.2)
	Others	36 (14.9)
	Missing	10 (4.2)

Table 2.

Item-analysis

Item	M±SD	Corrected item-total correlation
1. Guardians gave me some information about safety.	3.53±0.70	.71
2. Guardians told me to be careful about water, electricity, and fire.	3.55±0.69	.74
3. Guardians told me to be careful when crossing the street.	3.53±0.73	.74
4. Guardians told me what to do when I’m in danger.	3.61±0.65	.71
5. Nobody is at home to take care of me or to protect me.	3.74±0.67	.43
6. Guardians warned me not to play with matches, lighters, knives, or sharp things.	3.77±0.58	.48
7. Guardians locked me in the house alone.	3.67±0.64	.70
8. Guardians ignored me when told I was being bullied by my peers.	3.75±0.70	.51
9. When I was a child, I was often left alone by myself.	3.74±0.63	.41

SD, standard deviation.

Table 3.

Results of model fit tests for different models

Model	MI	χ²/df	RMSEA	IFI	TLI	CFI
Original model		3.42	.10	.95	.93	.95
Error covariance model for items 1 and 2	11.22	2.73	.08	.97	.95	.97
Error covariance model for items 2 and 3	10.51	2.48	.08	.97	.96	.97

CFI, comparative fit index; IFI, incremental fit index; MI, modification index; RMSEA, root mean square error of approximation; SRMR, standardized root mean squared residual; TLI, Tucker-Lewis index.

Table 4.

Convergent validity test

Domain	Estimate	SE	p	CR	AVE	Cronbach α	Inter-subscale correlation
Factor 1				.93	.73	.93	.39
1	0.87
2	0.88	0.05	<.001
3	0.91	0.06	<.001
4	0.82	0.05	<.001
7	0.78	0.06	<.001
Factor 2				.79	.48	.78
5	0.68
6	0.75	0.11	<.001
8	0.60	0.12	<.001
9	0.74	0.11	<.001
Total						.87

AVE, average variance extracted; CR, composite reliability; SE, standard error.

References

1. Crosson-Tower C. Understanding child abuse and neglect. 10th ed. Pearson; 2021.
2. Slack KS, Holl J, Altenbernd L, McDaniel M, Stevens AB. Improving the measurement of child neglect for survey research: issues and recommendations. Child Maltreat. 2003;8(2):98-111. https://doi.org/10.1177/1077559502250827Article PubMed
3. English DJ, Thompson R, Graham JC, Briggs EC. Toward a definition of neglect in young children. Child Maltreat. 2005;10(2):190-206. https://doi.org/10.1177/1077559505275178Article PubMed
4. Daniel B. Why have we made neglect so complicated?: taking a fresh look at noticing and helping the neglected child. Child Abuse Rev. 2015;24(2):82-94. https://doi.org/10.1002/car.2296Article
5. Qin J, Wang X, Chen C. Psychometric properties of the child neglect scale and risk factors for child neglect in Chinese young males who were incarcerated. Int J Environ Res Public Health. 2023;20(5):4659. https://doi.org/10.3390/ijerph20054659Article PubMed PMC
6. Yang S. Child abuse scale and child neglect scale: development, reliability and validity [dissertation]. Hunan: Central South University; 2006.
7. Golden MH, Samuels MP, Southall DP. How to distinguish between neglect and deprivational abuse. Arch Dis Child. 2003;88(2):105-107. https://doi.org/10.1136/adc.88.2.105Article PubMed PMC
8. Corby B, Shemmings D, Wilkins D. A history of child abuse and neglect 1870-2000. In: Corby B, Shemmings D, Wilkins D, editors. Child abuse: an evidence base for confident practice. Open University Press; 2012. p. 32-44.
9. Haworth S, Schaub J, Kidney E, Montgomery P. A systematic review of measures of child neglect. Res Soc Work Pract. 2022;34(1):17-40. https://doi.org/10.1177/10497315221138066Article
10. Enskär K, Isma GE, Rämgård M. Safe environments: through the eyes of 9-year-old schoolchildren from a socially vulnerable area in Sweden. Child Care Health Dev. 2021;47(1):57-69. https://doi.org/10.1111/cch.12809Article PubMed
11. World Health Organization. Children’s environmental health [Internet]. World Health Organization; 2020 [cited 2025 Sep 15]. Available from: https://www.who.int/health-topics/children-environmental-health#tab=tab_1
12. Evans GW. Child development and the physical environment. Annu Rev Psychol. 2006;57:423-451. https://doi.org/10.1146/annurev.psych.57.102904.190057Article
13. Chan M. Linking child survival and child development for health, equity, and sustainable development. Lancet. 2013;381(9877):1514-1515. https://doi.org/10.1016/S0140-6736(13)60944-7Article PubMed
14. Bailhache M, Leroy V, Pillet P, Salmi LR. Is early detection of abused children possible?: a systematic review of the diagnostic accuracy of the identification of abused children. BMC Pediatr. 2013;13:202. https://doi.org/10.1186/1471-2431-13-202Article PMC
15. National Institute for Health and Care Excellence. Child abuse and neglect: recognising, assessing and responding to abuse and neglect of children and young people [Internet]. National Institute for Health and Care Excellence; 2017 [cited 2025 Sep 15]. Available from: https://www.nice.org.uk/guidance/ng76
16. Bernstein DP, Fink L. Childhood Trauma Questionnaire: a retrospective self-report manual. The Psychological Corporation; 1998.
17. Zhao X, Zhang Y, Li L. Childhood abuse questionnaire for 435 children. Chin J Clin Psychol. 2004;12(4):377-379.
18. He J, Zhong X, Gao Y, Xiong G, Yao S. Psychometric properties of the Chinese version of the Childhood Trauma Questionnaire-Short Form (CTQ-SF) among undergraduates and depressive patients. Child Abuse Negl. 2019;91:102-108. https://doi.org/10.1016/j.chiabu.2019.03.009Article
19. Hu C, Kong M. Influence of child neglect on children sense of loneliness: mediating effect of self-esteem. Chin J Health Psychol. 2022;30(2):223-227. https://doi.org/10.13342/j.cnki.cjhp.2022.02.013Article
20. Riley AW. Evidence that school-age children can self-report on their health. Ambul Pediatr. 2004;4(4 Suppl):371-376. https://doi.org/10.1367/A03-178R.1Article PubMed
21. Stone WL, Lemanek KL. Developmental issues in children’s self-reports. In: La Greca AM, editor. Through the eyes of the child: obtaining self-reports from children and adolescents. Allyn & Bacon; 1990. p. 18-56.
22. Costello AB, Osborne JW. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval. 2005;10(7):1-9. https://doi.org/10.7275/jyj1-4868Article
23. Kline RB. Model testing and indexing. In: Kline RB, editor. Principles and practice of structural equation modeling. 5th ed. Guilford publications; 2023. p. 156-177.
24. de Vet HC, Terwee CB, Mokkink LB, Knol DL. Field-testing: item reduction and data structure. In: de Vet HC, Terwee CB, Mokkink LB, Knol DL, editors. Measurement in medicine: a practical guide. Cambridge University Press; 2011. p. 65-95.
25. Hu L, Bentler PM. Evaluating model fit. In: Hoyle RH, editor. Structural equation modeling: concepts, issues, and applications. Sage; 1995. p. 76-99.
26. Bentler PM. Reliability and practical fit. In: Bentler PM, editor. EQS 6 structural equations program manual. Multivariate Software; 1995. p. 351-369.
27. Bae BR. Structural equation modeling with Amos 24. Chenngram Books; 2017.
28. Park EJ, Kim HY, Nho JH, Ko E, Boyes AW. Validity and reliability of the Korean version of Supportive Care Needs Survey-Short Form 34 for patients with cancer: a methodological study. Asian Nurs Res (Korean Soc Nurs Sci). 2025;19(1):60-68. https://doi.org/10.1016/j.anr.2024.12.003Article PubMed
29. Polit DF, Beck CT. Analyzing qualitative data. In: Polit DF, Beck CT, editors. Nursing research: generating and assessing evidence for nursing practice. Lippincott Williams & Wilkins; 2008. p. 508-522.
30. Bang KS, Kim S, Kim W, Choi S, Jeong Y, Choe JH. Validity and reliability of the Korean version of the Trauma-Informed Climate Scale-10. Asian Nurs Res (Korean Soc Nurs Sci). 2024;18(5):460-467. https://doi.org/10.1016/j.anr.2024.10.002Article PubMed
31. Cole DA, Ciesla JA, Steiger JH. The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis. Psychol Methods. 2007;12(4):381-398. https://doi.org/10.1037/1082-989X.12.4.381Article
32. Hermida R. The problem of allowing correlated errors in structural equation modeling: concerns and considerations. Comput Methods Soc Sci. 2015;3(1):5-17.
33. Streiner DL, Norman GR, Cairney J. Reliability. In: Streiner DL, Norman GR, Cairney J, editors. Health measurement scales: a practical guide to their development and use. Oxford university press; 2024. p. 196-197.
34. Chen K, Li X, Zheng J, Ma X, Zhao Q, Xia J, et al. Development of a theoretical framework for the creation of assessment tools on parental literacy in child unintentional injury prevention: a literature-based expert consultation study. Chin Public Health. 2024;40(4):408-412. https://doi.org/10.11847/zgggws1142470Article
35. Zhou Y, Jiang Y, Li S. Research progress on influencing factors of accidental injuries of children. Intell Health. 2024;10(21):28-31. https://doi.org/10.19335/j.cnki.2096-1219.2024.21.009Article
36. Gu C, Wang L. Research progress in understanding peer victimization of children and adolescents. Chin J Sch Health. 2022;43(3):476-480. https://doi.org/10.16835/j.cnki.1000-9817.2022.03.038Article
37. Wang C, Zhou Y, Jin R. Research progress on the education system for the prevention of child sexual abuse. Chin Prim Health Care. 2021;35(5):42-46.Article
38. Engström M, Hiltunen J, Wallby T, Lucas S. Child Health Nurses’ experiences of addressing psychosocial risk factors with the families they meet. Acta Paediatr. 2021;110(2):574-583. https://doi.org/10.1111/apa.15492Article PubMed
39. Flemington T, Lock M, Shipp J, Hartz D, Lonne B, Fraser JA. Cultural safety and child protection responses in hospitals: a scoping review. Int J Child Maltreat. 2021;4(1):5-33. https://doi.org/10.1007/s42448-020-00065-3Article

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Validity and reliability of the Security Neglect Subscale of the Child Neglect Scale in vulnerable Chinese children: a methodological study

DOI: https://doi.org/10.4040/jkan.25089

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Validity and reliability of the Security Neglect Subscale of the Child Neglect Scale in vulnerable Chinese children: a methodological study

Characteristic	Category	N (%)
Gender	Women	108 (44.6)
	Men	134 (55.4)
Age (yr)	5–12	113 (46.7)
	13–18	129 (53.3)
Education	Primary school or less	104 (43.0)
	Junior school	80 (33.1)
	High school	45 (18.6)
	Dropout	2 (0.8)
	Missing	11 (4.5)
Family type	Complete family	30 (12.4)
	Orphan	49 (20.2)
	Single-parent family	114 (47.1)
	Step-family	3 (1.2)
	Others	36 (14.9)
	Missing	10 (4.2)

Item	M±SD	Corrected item-total correlation
1. Guardians gave me some information about safety.	3.53±0.70	.71
2. Guardians told me to be careful about water, electricity, and fire.	3.55±0.69	.74
3. Guardians told me to be careful when crossing the street.	3.53±0.73	.74
4. Guardians told me what to do when I’m in danger.	3.61±0.65	.71
5. Nobody is at home to take care of me or to protect me.	3.74±0.67	.43
6. Guardians warned me not to play with matches, lighters, knives, or sharp things.	3.77±0.58	.48
7. Guardians locked me in the house alone.	3.67±0.64	.70
8. Guardians ignored me when told I was being bullied by my peers.	3.75±0.70	.51
9. When I was a child, I was often left alone by myself.	3.74±0.63	.41

Model	MI	χ²/df	RMSEA	IFI	TLI	CFI
Original model		3.42	.10	.95	.93	.95
Error covariance model for items 1 and 2	11.22	2.73	.08	.97	.95	.97
Error covariance model for items 2 and 3	10.51	2.48	.08	.97	.96	.97

Domain	Estimate	SE	p	CR	AVE	Cronbach α	Inter-subscale correlation
Factor 1				.93	.73	.93	.39
1	0.87
2	0.88	0.05	<.001
3	0.91	0.06	<.001
4	0.82	0.05	<.001
7	0.78	0.06	<.001
Factor 2				.79	.48	.78
5	0.68
6	0.75	0.11	<.001
8	0.60	0.12	<.001
9	0.74	0.11	<.001
Total						.87

Table 1. Descriptive characteristics of participants (N=242)

Table 2. Item-analysis

SD, standard deviation.

Table 3. Results of model fit tests for different models

CFI, comparative fit index; IFI, incremental fit index; MI, modification index; RMSEA, root mean square error of approximation; SRMR, standardized root mean squared residual; TLI, Tucker-Lewis index.