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The Construction and Validation of the SP-IE Questionnaire: An Instrument for Measuring Spatial Presence in Immersive Environments

机译:SP-IE问卷的构建和验证:沉浸环境中的空间存在测量工具

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The present study describes the construction and validation of an instrument for measuring spatial presence (the sense of 'being there') in the context of highly immersive environments: the SP-IE [Spatial Presence for Immersive Environments] questionnaire, for use in the French-speaking community. A first raw version of the questionnaire was submitted to an item selection procedure and reliability tests with 67 participants. An exploratory factor analysis (EFA) with 179 participants was then employed on the resulting version to explore its underlying scales. Finally, the outcome scale-structure from the EFA was evaluated using confirmatory factor analyses (CFAs). This process resulted in a well-structured 20-item questionnaire (link), based on seven scales: (ⅰ) the sense of spatial presence, (ⅱ) the affordance of the environment, (ⅲ) the user's enjoyment, (ⅳ) the user's attention allocation to the task, (ⅴ) the sense of reality attributed to the environment, (ⅵ) the social embodiment with avatars, and (ⅶ) the possible negative effects of the environment (cybersickness). Results showed overall good internal consistency and satisfactory convergent validity of the scales. The fit indexes obtained (X~2/ df = 1.34, GFI = .95, CFI = .90, TLI = .87, SRMR = .068, and RSMEA = .045) demonstrated a good fitness of the structure proposed. However, even though the scale structure proposed in this paper was confirmed, its low discriminant validity encourages further evaluations.
机译:本研究描述了一种在高度沉浸式环境中测量空间存在(“存在”的感觉)的工具的构造和验证:SP-IE(沉浸式环境的空间存在)问卷,用于法国社区。问卷的第一个原始版本已提交给项目选择程序和67位参与者进行了可靠性测试。然后在结果版本中使用了179名参与者进行探索性因素分析(EFA),以探索其潜在量表。最后,使用验证性因子分析(CFAs)评估了全民教育的结局量表结构。此过程基于七个等级得出了结构合理的20个项目的问卷(链接):(ⅰ)空间存在感,(ⅱ)环境承受能力,(ⅲ)用户的享受,(ⅳ)用户对任务的注意力分配,(ⅴ)归因于环境的现实感,(ⅵ)具有化身的社交体现,以及(ⅶ)环境的可能负面影响(网络病)。结果显示总体良好的内部一致性和令人满意的收敛效度。获得的拟合指数(X〜2 / df = 1.34,GFI = .95,CFI = .90,TLI = .87,SRMR = .068,RSMEA = .045)表明所提出的结构具有良好的适应性。然而,尽管本文提出的量表结构得到了证实,但其低判别效度仍鼓励进行进一步的评估。

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