目的汉化孕妇尿失禁知信行量表(knowledge,attitude and practice assessment scale for pregnant women on UI,KAP-IU)并评价其信度、效度,为医护人员对孕妇尿失禁知信行情况的评估提供测量工具。方法2022年12月—2023年3月按照国际通...目的汉化孕妇尿失禁知信行量表(knowledge,attitude and practice assessment scale for pregnant women on UI,KAP-IU)并评价其信度、效度,为医护人员对孕妇尿失禁知信行情况的评估提供测量工具。方法2022年12月—2023年3月按照国际通用量表跨文化调适流程对量表进行汉化,形成中文版KAP-IU量表。2023年3月—6月采用便利抽样选取门诊就诊孕妇456例进行调查,基于Rasch模型和经典测量学检验指标进行信度、效度检验。结果430例孕妇完成研究。中文版KAP-IU量表共由23个条目构成,符合Rasch模型单维性假设,Rasch模型解释了66.1%的变异;个人和条目的分隔信度均>0.8,分隔指数≈3,所有条目加权的均方拟合统计量(information-weighted mean square fit statistic,Infit MNSQ)和未加权的均方拟合统计量(unweighted mean square fit statistic,Outfit MNSQ)均方值在0.473~1.611之间,点测量相关系数(point-measure correlation,Pt-Measure Corr)在0.293~0.731之间,个体能力均值比条目难度均值高0.343 Logits;总量表Cronbachα系数为0.783,3个维度(知识、态度和行为)的重测信度分别为0.751、0.815、0.760;条目水平内容效度指数(item-level CVI,I-CVI)为0.810~1.000;量表水平内容效度(scale-level CVI,S-CVI)为0.824。结论中文版KAP-IU量表在国内孕妇人群中经验证信度、效度良好,难度适中,可作为孕妇尿失禁知信行情况评估的可靠工具。展开更多
Psychometric theory requires unidimensionality (i.e., scale items should represent a common latent variable). One advocated approach to test unidimensionality within the Rasch model is to identify two item sets from a...Psychometric theory requires unidimensionality (i.e., scale items should represent a common latent variable). One advocated approach to test unidimensionality within the Rasch model is to identify two item sets from a Principal Component Analysis (PCA) of residuals, estimate separate person measures based on the two item sets, compare the two estimates on a person-by-person basis using t-tests and determine the number of cases that differ significantly at the 0.05-level;if ≤5% of tests are significant, or the lower bound of a binomial 95% confidence interval (CI) of the observed proportion overlaps 5%, then it is suggested that strict unidimensionality can be inferred;otherwise the scale is multidimensional. Given its proposed significance and potential implications, this procedure needs detailed scrutiny. This paper explores the impact of sample size and method of estimating the 95% binomial CI upon conclusions according to recommended conventions. Normal approximation, “exact”, Wilson, Agresti-Coull, and Jeffreys binomial CIs were calculated for observed proportions of 0.06, 0.08 and 0.10 and sample sizes from n= 100 to n= 2500. Lower 95%CI boundaries were inspected regarding coverage of the 5% threshold. Results showed that all binomial 95% CIs included as well as excluded 5% as an effect of sample size for all three investigated proportions, except for the Wilson, Agresti-Coull, and JeffreysCIs, which did not include 5% for any sample size with a 10% observed proportion. The normal approximation CI was most sensitive to sample size. These data illustrate that the PCA/t-test protocol should be used and interpreted as any hypothesis testing procedure and is dependent on sample size as well as binomial CI estimation procedure. The PCA/t-test protocol should not be viewed as a “definite” test of unidimensionality and does not replace an integrated quantitative/qualitative interpretation based on an explicit variable definition in view of the perspective, context and purpose of measurement.展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numer...Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.展开更多
儿童早期数学能力评估对数学能力的发展研究具有重要意义,研究修订了《早期数学能力评估工具》(Research-Based Early Math Assessment,REMA),并对其信度和效度进行检验.研究以上海市两所幼儿园313名儿童为研究对象,采用项目反应理论中...儿童早期数学能力评估对数学能力的发展研究具有重要意义,研究修订了《早期数学能力评估工具》(Research-Based Early Math Assessment,REMA),并对其信度和效度进行检验.研究以上海市两所幼儿园313名儿童为研究对象,采用项目反应理论中的Rasch模型检验REMA的信效度.结果表明,REMA的信度较好,基本为单一的能力维度结构,怀特图说明量表整体适合中高水平的被试,各个项目的内外适合度指标在0.5~1.5之间,符合Rasch模型,早期数学能力与数学学习品质呈中高水平相关(相关系数在0.34~0.61之间).研究表明,REMA量表具有良好的信效度,适合作为评估3~6岁学前儿童数学能力的有效工具.展开更多
文摘目的汉化孕妇尿失禁知信行量表(knowledge,attitude and practice assessment scale for pregnant women on UI,KAP-IU)并评价其信度、效度,为医护人员对孕妇尿失禁知信行情况的评估提供测量工具。方法2022年12月—2023年3月按照国际通用量表跨文化调适流程对量表进行汉化,形成中文版KAP-IU量表。2023年3月—6月采用便利抽样选取门诊就诊孕妇456例进行调查,基于Rasch模型和经典测量学检验指标进行信度、效度检验。结果430例孕妇完成研究。中文版KAP-IU量表共由23个条目构成,符合Rasch模型单维性假设,Rasch模型解释了66.1%的变异;个人和条目的分隔信度均>0.8,分隔指数≈3,所有条目加权的均方拟合统计量(information-weighted mean square fit statistic,Infit MNSQ)和未加权的均方拟合统计量(unweighted mean square fit statistic,Outfit MNSQ)均方值在0.473~1.611之间,点测量相关系数(point-measure correlation,Pt-Measure Corr)在0.293~0.731之间,个体能力均值比条目难度均值高0.343 Logits;总量表Cronbachα系数为0.783,3个维度(知识、态度和行为)的重测信度分别为0.751、0.815、0.760;条目水平内容效度指数(item-level CVI,I-CVI)为0.810~1.000;量表水平内容效度(scale-level CVI,S-CVI)为0.824。结论中文版KAP-IU量表在国内孕妇人群中经验证信度、效度良好,难度适中,可作为孕妇尿失禁知信行情况评估的可靠工具。
文摘Psychometric theory requires unidimensionality (i.e., scale items should represent a common latent variable). One advocated approach to test unidimensionality within the Rasch model is to identify two item sets from a Principal Component Analysis (PCA) of residuals, estimate separate person measures based on the two item sets, compare the two estimates on a person-by-person basis using t-tests and determine the number of cases that differ significantly at the 0.05-level;if ≤5% of tests are significant, or the lower bound of a binomial 95% confidence interval (CI) of the observed proportion overlaps 5%, then it is suggested that strict unidimensionality can be inferred;otherwise the scale is multidimensional. Given its proposed significance and potential implications, this procedure needs detailed scrutiny. This paper explores the impact of sample size and method of estimating the 95% binomial CI upon conclusions according to recommended conventions. Normal approximation, “exact”, Wilson, Agresti-Coull, and Jeffreys binomial CIs were calculated for observed proportions of 0.06, 0.08 and 0.10 and sample sizes from n= 100 to n= 2500. Lower 95%CI boundaries were inspected regarding coverage of the 5% threshold. Results showed that all binomial 95% CIs included as well as excluded 5% as an effect of sample size for all three investigated proportions, except for the Wilson, Agresti-Coull, and JeffreysCIs, which did not include 5% for any sample size with a 10% observed proportion. The normal approximation CI was most sensitive to sample size. These data illustrate that the PCA/t-test protocol should be used and interpreted as any hypothesis testing procedure and is dependent on sample size as well as binomial CI estimation procedure. The PCA/t-test protocol should not be viewed as a “definite” test of unidimensionality and does not replace an integrated quantitative/qualitative interpretation based on an explicit variable definition in view of the perspective, context and purpose of measurement.
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
文摘Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.
文摘儿童早期数学能力评估对数学能力的发展研究具有重要意义,研究修订了《早期数学能力评估工具》(Research-Based Early Math Assessment,REMA),并对其信度和效度进行检验.研究以上海市两所幼儿园313名儿童为研究对象,采用项目反应理论中的Rasch模型检验REMA的信效度.结果表明,REMA的信度较好,基本为单一的能力维度结构,怀特图说明量表整体适合中高水平的被试,各个项目的内外适合度指标在0.5~1.5之间,符合Rasch模型,早期数学能力与数学学习品质呈中高水平相关(相关系数在0.34~0.61之间).研究表明,REMA量表具有良好的信效度,适合作为评估3~6岁学前儿童数学能力的有效工具.
