Discuss recommendations for best practices in Structural Equation Modeling.

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Discuss recommendations for best practices in StructuralEquation Modeling.

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The most commonly used estimation method in SEM is maximumlikelihood ML ML has various assumptions including 1 There will be no missing data 2 The endogenous or dependent variables will have a multivariate normal distribution If a complete data file is unavailable then the researcher must test whether the data are missing completely at random MCAR missing at random MAR or missing not at random MNAR If data are MCAR then the missingness on the variable of interest is unrelated to any of the variables in the data set If the data are MAR then systematic differences may exist between missing and observed values However these differences are accounted for by other variables in the data set Finally if data are MNAR then there is a systematic pattern to the missing data the presence or absence of a score on variable X is related to the variable itself To determine whether data are MCAR Littles MCAR test can be used ie a statistically nonsignificant p value 05 denotes that data are MCAR If Littles test is statistically significant then data may be MAR or MNAR further investigation of participants with missing data is required If data are MCAR or MAR the sample is large and the proportion of missing data is modest 5 list wise deletion is a reasonable option An alternative approach is using Multiple Imputation MI to estimate missing data Finally when data are MNAR item parcels may be useful though for the novice practitioner SEM would not be recommended Having data that are multivariate normal is a key assumption when performing SEM using the ML default Although univariate normality does not guarantee the multivariate normality of ones data we recommend that each variable be scrutinized to identify any deviations from a normal distribution We provide a straightforward overview of the primary visual and statistical techniques that may be used to gauge univariate See Answer

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