scholarly journals Normal Theory GLS Estimator for Missing Data: An Application to Item-Level Missing Data and a Comparison to Two-Stage ML

2017 ◽  
Vol 8 ◽  
Author(s):  
Victoria Savalei ◽  
Mijke Rhemtulla
Keyword(s):  
Missing Data ◽  
Two Stage ◽  
Normal Theory ◽  
Item Level

scholarly journals Two-stage maximum likelihood approach for item-level missing data in regression

2020 ◽  
Vol 52 (6) ◽  
pp. 2306-2323 ◽  
Author(s):  
Lihan Chen ◽  
Victoria Savalei ◽  
Mijke Rhemtulla
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
High Efficiency ◽  
Structural Equation Models ◽  
Small Sample ◽  
Parameter Estimates ◽  
Two Stage ◽  
Item Level ◽  
Scale Scores

AbstractPsychologists use scales comprised of multiple items to measure underlying constructs. Missing data on such scales often occur at the item level, whereas the model of interest to the researcher is at the composite (scale score) level. Existing analytic approaches cannot easily accommodate item-level missing data when models involve composites. A very common practice in psychology is to average all available items to produce scale scores. This approach, referred to as available-case maximum likelihood (ACML), may produce biased parameter estimates. Another approach researchers use to deal with item-level missing data is scale-level full information maximum likelihood (SL-FIML), which treats the whole scale as missing if any item is missing. SL-FIML is inefficient and it may also exhibit bias. Multiple imputation (MI) produces the correct results using a simulation-based approach. We study a new analytic alternative for item-level missingness, called two-stage maximum likelihood (TSML; Savalei & Rhemtulla, Journal of Educational and Behavioral Statistics, 42(4), 405–431. 2017). The original work showed the method outperforming ACML and SL-FIML in structural equation models with parcels. The current simulation study examined the performance of ACML, SL-FIML, MI, and TSML in the context of univariate regression. We demonstrated performance issues encountered by ACML and SL-FIML when estimating regression coefficients, under both MCAR and MAR conditions. Aside from convergence issues with small sample sizes and high missingness, TSML performed similarly to MI in all conditions, showing negligible bias, high efficiency, and good coverage. This fast analytic approach is therefore recommended whenever it achieves convergence. R code and a Shiny app to perform TSML are provided.

scholarly journals Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

2017 ◽  
Vol 42 (4) ◽  
pp. 405-431 ◽  
Author(s):  
Victoria Savalei ◽  
Mijke Rhemtulla
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Estimation Method ◽  
Analytic Approach ◽  
Parameter Estimates ◽  
Two Stage ◽  
Data Set ◽  
Item Level

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data—that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.

scholarly journals PC program extending the two-stage polynomial growth curve model to allow missing data

1993 ◽  
Vol 33 (3-4) ◽  
pp. 287-296 ◽  
Author(s):  
Amy M. Furey ◽  
Thomas R. Ten Have ◽  
Charles J. Kowalski ◽  
Emet D. Schneiderman ◽  
Stephen M. Willis
Keyword(s):  
Missing Data ◽  
Growth Curve ◽  
Polynomial Growth ◽  
Growth Curve Model ◽  
Two Stage ◽  
Curve Model

Multistep autoregressive reconstruction of seismic records

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. V111-V118 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Mauricio D. Sacchi
Keyword(s):  
Missing Data ◽  
Field Data ◽  
Linear Prediction ◽  
Fourier Method ◽  
Weighted Norm ◽  
Sampled Data ◽  
Regular Grid ◽  
Two Stage ◽  
Low Frequencies ◽  
Seismic Records

Linear prediction filters in the [Formula: see text] domain are widely used to interpolate regularly sampled data. We study the problem of reconstructing irregularly missing data on a regular grid using linear prediction filters. We propose a two-stage algorithm. First, we reconstruct the unaliased part of the data spectrum using a Fourier method (minimum-weighted norm interpolation). Then, prediction filters for all the frequencies are extracted from the reconstructed low frequencies. The latter is implemented via a multistep autoregressive (MSAR) algorithm. Finally, these prediction filters are used to reconstruct the complete data in the [Formula: see text] domain. The applicability of the proposed method is examined using synthetic and field data examples.

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