共查询到20条相似文献，搜索用时 15 毫秒
1.
David Goretzko 《Educational and psychological measurement》2022,82(3):444
Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process. Hence, in this study, we evaluated the performance of different factor retention criteria—the Factor Forest, parallel analysis based on a principal component analysis as well as parallel analysis based on the common factor model and the comparison data approach—in combination with different missing data methods, namely an expectation-maximization algorithm called Amelia, predictive mean matching, and random forest imputation within the multiple imputations by chained equations (MICE) framework as well as pairwise deletion with regard to their accuracy in determining the number of factors when data are missing. Data were simulated for different sample sizes, numbers of factors, numbers of manifest variables (indicators), between-factor correlations, missing data mechanisms and proportions of missing values. In the majority of conditions and for all factor retention criteria except the comparison data approach, the missing data mechanism had little impact on the accuracy and pairwise deletion performed comparably well as the more sophisticated imputation methods. In some conditions, especially small-sample cases and when comparison data were used to determine the number of factors, random forest imputation was preferable to other missing data methods, though. Accordingly, depending on data characteristics and the selected factor retention criterion, choosing an appropriate missing data method is crucial to obtain a valid estimate of the number of factors to extract. 相似文献
2.
王雅丽 《浙江教育学院学报》2011,(1):106-112
目的：对非学业自我描述量表进行修订和信效度检验.方法：以浙江师范大学492名学生为被试,使用SPSS16.0和Amos5.0进行探索性和验证性因素分析.结果表明：（1）经过探索性因素分析,量表包含7个因子,共45个项目,各个项目负荷在0.416~0.810之间,7个二级因子解释了总方差的54.611%;（2）将7个二级因子按Marsh的多层次自我描述量表依托的理论合并为4个一级因子,提高了内部一致性水平,4个因子及总量表内部一致性系数为0.651~0.821.各因子间相关呈中等偏低相关,结构效度良好;（3）通过验证性因素分析,4个一级因子的模型拟合优度指标分别为：χ2/df〈5,RMSEA〈0.05,GFI,NFI,TLI,CFI为0.989~0.997;（4）在体能、与双亲关系和一般自我3个因子上存在性别的显著性差异,男生比女生评价更积极.鉴于以上结果,非学业自我描述量表的信效度水平达到了测量学的要求. 相似文献
3.
英语、物理、高数、线代、概率论基础、概率论与数理统计等高校公共基础课程旨在帮助学生建立科学的知识结构,培养学生基本的思维能力、分析解决问题的能力和英语运用能力,为后期专业课程的学习奠定基础,因此这类课程的教学质量与教学效果具有尤为重要的意义。本文利用探索性数据分析方法对某高校2002-2009级学生公共基础课程的考试成绩进行了统计分析(采用SPSS软件),以期通过成绩状况探讨此类课程的教学效果及其影响因素,并提出此类课程教学改革的建议。 相似文献
4.
在有关当代大学生学习适应状况研究的基础上,探讨了大学生学习适应状况的主要因素,编制了一套适合当代大学生学习适应状况的量表,对9所院校900名大学生进行施测,对所得数据进行探索性因素分析。结果表明,大学生学习适应状况的主要因素包括:学习方法、学习热情、学习态度、专业兴趣、学习动力、学习环境等六个因素。验证性因素分析结果表明,所提取的6个因素与构想模型拟合较好,测验具有较好的信效度。 相似文献
5.
Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework. 相似文献
6.
为了解家庭教育中“攻击性”这一特征,有针对性地为实施健康家庭教育提供依据,在文献分析、访谈、开放式问卷等方法的基础上收集项目,采用探索性因素分析获得父亲攻击型教养方式七因子模型：督促攻击、默许对朋辈的攻击、责罚、惩罚对长辈的攻击、默许对长辈的攻击、攻击示范、攻击性教养观。此模型在验证性因素分析中得到了验证,具有良好的信度和效度,这对于我国家庭教养方式本土化研究提供了一定理论基础。 相似文献
7.
Kenneth J. Smith Jeanette A. Davy Donald L. Rosenberg 《Assessment in Education: Principles, Policy & Practice》2012,19(2):231-250
The purpose of this study was to examine an alternative configuration of the Academic Motivation Scale (AMS), a psychometric instrument used as a measure of academic motivation in various academic environments. The analyses utilised data from a convenience sample of 2354 business students, broken into two random subsamples of 1177 cases. Exploratory factor analysis of the 28 AMS scale items was conducted on the estimation sample. The results indicated good model fit for a four-factor configuration consisting of amotivation, external regulation, identified regulation, and intrinsic motivation as indicated by factor loadings, as well as internal consistency and reliability statistics.
Confirmatory factor analysis was conducted on the holdout sample to independently assess the construct validity and internal consistency of the item loadings on the reconfigured scale. Results indicated good model fit for the four-factor configuration and a significant loss of fit for competing three- and one-factor models. The uncovered factor structure advances our understanding of how the items on this scale cluster into theoretically meaningful constructs. This knowledge may be utilised in research designed to further assess the impact of motivational states on educational outcomes such as academic performance, absenteeism, dropout rates, etc. 相似文献
8.
探索积极人格特质问卷(PPTQ)在中国大学生中的因素结构。采用积极人格特质问卷(Positive Personality Traits Questionnaire)中文版,对648名大学生施测,对其中一半数据使用PASWStatistics18进行探索性因素分析,另一半数据使用AMOS16.0进行验证性因素分析(CFA)。探索性因素分析得出积极自我意象、外向性和文化认同三因素结构。累计解释率为50.457%,验证性因素分析结果显示:χ2/df=2.230,GFI=0.841。AGFI=0.816,CFI=0.822,RMSEA=0.062,中文版三因素结构在中国大学生人群中较为合理。 相似文献
9.
10.
通过3个研究:魅力型教师特征的收集;魅力型教师的特质分析;魅力型教师特质结构的验证性因素分析,结果显示:魅力型教师是一个一阶5因素结构,分别为亲和力、教学能力、创新能力、思想品行、专业素养。 相似文献
11.
Edward E. Rigdon 《Structural equation modeling》2013,20(1):87-91
Amos 3.51. Written by James L. Arbuckle. Distributed by SmallWaters Corporation, 1507 East 53rd Street, Suite 452, Chicago, IL 60615. Regular price, $465, educational price, $325, possible upgrade discounts. 相似文献
12.
Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences and its relation to traditional statistical methods and structural equation modeling. Real data are used for illustration. 相似文献
13.
We provide a brief overview of two R packages that can conduct exploratory factor analysis (EFA): psych and EFAutilities. After introducing EFA and the exemplar data used in this paper we discuss best practices for EFA. Next, we describe the approaches used in the two packages for EFA. During this explanation, we provide sample code and discuss the usage and results of two empirical datasets. Finally, we highlight the similarities and distinctions of each package on modeling EFA. 相似文献
14.
A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make judgments about the number of factors to extract. Implicit in this recommendation is that, when the number of factors is chosen based on PA, uncertainty nevertheless exists. We propose a Bayesian parallel analysis (B-PA) method to incorporate the uncertainty with decisions about the number of factors. B-PA yields a probability distribution for the various possible numbers of factors. We implement and compare B-PA with a frequentist approach, revised parallel analysis (R-PA), in the contexts of real and simulated data. Results show that B-PA provides relevant information regarding the uncertainty in determining the number of factors, particularly under conditions with small sample sizes, low factor loadings, and less distinguishable factors. Even if the indicated number of factors with the highest probability is incorrect, B-PA can show a sizable probability of retaining the correct number of factors. Interestingly, when the mode of the distribution of the probabilities associated with different numbers of factors was treated as the number of factors to retain, B-PA was somewhat more accurate than R-PA in a majority of the conditions. 相似文献
15.
The purpose of the present study was to validate an existing school environment instrument, the School Level Environment Questionnaire (SLEQ). The SLEQ consists of 56 items, with seven items in each of eight scales. One thousand, one hundred and six (1106) teachers in 59 elementary schools in a southwestern USA public school district completed the instrument. An exploratory factor analysis was undertaken for a random sample of half of the completed surveys. Using principal axis factoring with oblique rotation, this analysis suggested that 13 items should be dropped and that the remaining 43 items could best be represented by seven rather than eight factors. A confirmatory factor analysis was run with the other half of the original sample using structural equation modeling. Examination of the fit indices indicated that the model came close to fitting the data, with goodness-of-fit (GOF) coefficients just below recommended levels. A second model was then run with two of the seven factors, with their associated items removed. That left five factors with 35 items. Model fit was improved. A third model was tried, using the same five factors with 35 items but with correlated residuals between some of the items within a factor. This model seemed to fit the data well, with GOF coefficients in recommended ranges. These results led to a refined, more parsimonious version of the SLEQ that was then used in a larger study. Future research is needed to see if this model would fit other samples in different elementary schools and in secondary schools both in the USA and in other countries. This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
16.
Nathaniel Owen Alison Fox Terese Bird 《International Journal of Research & Method in Education》2016,39(2):170-193
This paper documents the creation, implementation and analysis of a survey instrument designed to reveal patterns of use and attitudes towards the value of social media by UK teachers. The study was motivated to discover which teachers use social media professionally, how they use it (both personally and professionally) and attitudes to social media as a professional tool (for their students' and their own professional use). The instrument was created from verbal data from two focus group discussions regarding the use of social media in education. Attitude statements were included verbatim when practical. This instrument was placed online and practising teachers invited to complete it (n?=?216). Exploratory factor analysis and hierarchical clustering identified 9 factors from 54 attitude statements and 5 distinct teacher groups. The rich data allowed each group to be carefully defined, providing potentially invaluable information to school leaders when developing social media projects to recognize and accommodate the full range of teacher concerns and experience. The paper also addresses methodological concerns regarding instrument creation, dealing with missing data and the impact of missing data on subsequent analysis. 相似文献
17.
以红河学院为例对在校大学生的学习情况进行问卷调查,探索性因素分析结果表明:影响红河学院在校大学生学习情况的主要因素有学习规划和学习方法、学习态度、学习气氛、教师等其他因素。 相似文献
18.
运用探索性因子分析考察我校初等教育专业面向2010级学生所开设的32门核心课程的所属因子,对本专业人才培养方案中课程设置的内在联系以及结构合理性进行理论探讨。探索性因子分析表明,该专业开设的核心课程关联紧密、结构合理,可以进一步归属为基础理论、教材教法、综合技能、艺术素养等四个细微模块,体现了本专业着力提升学生内在素质以及教师技能的\"理论必须够用为度、突出实用与技能\"核心理念和教、学、做一体化的教学模式。 相似文献
19.
过去国内对主观幸福感(包括大学生)的研究更多是直接引用或修订国外编制的问卷或量表.所以引用过程中民族个性差异,人口统计量的差异,文化差异以及翻译等额外变量使得研究结论的准确性受到质疑,所以编制适用于中国大学生特点的问卷对主观幸福感的本土化研究有着重要意义. 相似文献
20.
以920名广州青少年及665名澳门青少年为被试,通过探索性因素分析和验证性因素分析,编制青少年价值观问卷,并考察两地青少年价值观的特点及其影响因素。结果表明：（1）青少年价值观问卷由社会观、人际观、家庭观、成就观、理想观、尊严观、生活观,及爱情观等八个因素组成;（2）问卷具有较好的信度和效度;（3）家庭观、成就观是两地青少年的主导价值观,但两地青少年在某些价值观维度的得分上存在差异;（4）两地青少年在“影响价值观形成的因素”的看法上存在一定的差异,但都将“家庭的影响”和“个人生活经历的变化”视为最重要的影响因素。 相似文献