Since the development of the original Support Intensity Scale—Adult Version (SIS-A) and the Support Intensity Scale—Child Version (SIS-C), the interest in supporting people with intellectual disabilities (ID) has changed. Resource allocation, better quality of resource utilization in the rehabilitation process, the development of support systems, and redefining the roles of organizations that support people with ID are just some of the changes. The aim of this study was to determine the factor structure of the SIS-C conducted on a sample of Bosnian–Herzegovinian (B&H) children (SISC B&H). The study included 377 children ID in B&H, aged 5-16. The data was analyzed with the SPSS 21 software (with the AMOS package). Exploratory factor analysis (EFA) was used to examine the factor structure of the SIS-C. Confirmatory factor analysis (CFA) was applied to determine the factor and constructive validity of SIS-C B&H. The CFA results indicated a poor fit of both the theoretical and empirical models even after modifications were made. The EFA showed the opposite results. This could be explained by the fact that within the factor solutions obtained from the EFA, various aslope or orthogonal models, linear or hierarchical, can be constructed. Among these models, some exhibited good fit to the data. Thus, data from the current study could be used to generate new hypotheses and deliver more conclusive answers.

中文翻译：

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波斯尼亚-黑塞哥维那样本支持强度量表的因子效度分析

自从最初的支持强度量表 - 成人版 (SIS-A) 和支持强度量表 - 儿童版 (SIS-C) 开发以来，对支持智障人士 (ID) 的兴趣发生了变化。资源分配、康复过程中更好的资源利用质量、支持系统的开发以及重新定义支持智障人士的组织的角色只是其中的一些变化。本研究的目的是确定对波斯尼亚-黑塞哥维那 (B&H) 儿童 (SISC B&H) 样本进行的 SIS-C 的因子结构。该研究包括 B&H 的 377 名 ID 儿童，年龄在 5-16 岁之间。数据用SPSS 21软件（带有AMOS包）进行分析。探索性因子分析 (EFA) 用于检查 SIS-C 的因子结构。应用验证性因素分析 (CFA) 来确定 SIS-C B&H 的因素和建设性有效性。CFA 结果表明，即使在进行修改后，理论模型和经验模型的拟合度也不高。全民教育显示出相反的结果。这可以解释为，在从 EFA 获得的因子解中，可以构建各种线性或分层的斜率或正交模型。在这些模型中，有些模型表现出对数据的良好拟合。因此，当前研究的数据可用于产生新的假设并提供更确定的答案。这可以解释为，在从 EFA 获得的因子解中，可以构建各种线性或分层的斜率或正交模型。在这些模型中，有些模型对数据表现出良好的拟合。因此，当前研究的数据可用于产生新的假设并提供更确定的答案。这可以解释为，在从 EFA 获得的因子解中，可以构建各种线性或分层的斜率或正交模型。在这些模型中，有些模型表现出对数据的良好拟合。因此，当前研究的数据可用于产生新的假设并提供更确定的答案。