oppor7plusm刷机教程 Chapter4 M-Plus 教程.doc

oppor7plusm刷机教程 Chapter4 M-Plus 教程 导读：就爱阅读网友为您分享以下“Chapter4 M-Plus 教程”资讯，希望对您有所帮助，感谢您对92的支持! Examples: Exploratory Factor Analysis CHAPTER 4 EXAMPLES: EXPLORATORY FACTOR ANALYSIS Exploratory factor analysis (EFA) is used to determine the number of continuous latent variables that are needed to explain the correlations among a set of observed variables. The continuous latent variables are referred to as factors, and the observed variables are referred to as factor indicators. In EFA, factor indicators can be continuous, censored, binary, ordered categorical (ordinal), counts, or combinations of these variable types. EFA can also be carried out using exploratory structural equation modeling (ESEM; Asparouhov Muthén, 2009a) when factor indicators are continuous, censored, binary, ordered categorical (ordinal), and combinations of these variable types. ESEM examples are shown under Confirmatory Factor Analysis. Several rotations are available using both orthogonal and oblique procedures. The algorithms used in the rotations are described in Jennrich and Sampson (1966), Browne (2001), Bernaards and Jennrich (2005), Browne et al. (2004), and Jennrich and Bentler (2011, 2012). Standard errors for the rotated solutions are available using algorithms described in Jennrich (1973, 1974, 2007). Cudeck and O?Dell (1994) discuss the benefits of standard errors for rotated solutions. All EFA models can be estimated using the following special features: ? Missing data ? Complex survey data ? Mixture modeling The default is to estimate the model under missing data theory using all available data. The LISTWISE option of the DATA command can be used to delete all observations from the analysis that have missing values on one or more of the analysis variables. Corrections to the standard errors and chi-square test of model fit that take into account stratification, non-independence of observations, and unequal probability of selection are obtained by using the