Two Recommended Solutions for Missing Data ... - The Analysis Factor [PDF]

Two methods for dealing with missing data,vast improvements over traditional approaches, have become available in mainst

3 downloads 10 Views 124KB Size

Recommend Stories


Missing Data Analysis
Never wish them pain. That's not who you are. If they caused you pain, they must have pain inside. Wish

applied missing data analysis
Keep your face always toward the sunshine - and shadows will fall behind you. Walt Whitman

Factor Analysis for Questionnaire Survey Data
Just as there is no loss of basic energy in the universe, so no thought or action is without its effects,

Missing Data
Just as there is no loss of basic energy in the universe, so no thought or action is without its effects,

Bayesian methods for missing data
Silence is the language of God, all else is poor translation. Rumi

PdF Python for Data Analysis
Pretending to not be afraid is as good as actually not being afraid. David Letterman

Missing data analysis and homogeneity test for Turkish precipitation
The beauty of a living thing is not the atoms that go into it, but the way those atoms are put together.

Evidence for two-factor theory
We may have all come on different ships, but we're in the same boat now. M.L.King

Recommended topics for discussion [PDF]
Diabetes mellitus is a severe chronic disease, affecting 6-11 percent of the populations aged. 30-64 and about 20 percent of those older than age 65 throughout the world. Diabetes is reaching pandemic proportions, largely owing to an increase in type

Idea Transcript


The Analysis Factor Home About Our Team Employment Our Privacy Policy Membership Statistically Speaking Membership Program Programs Center Login Workshops Programs Center Login Live Online Workshops On Demand Tutorials Consulting Free Webinars Contact Customer Login Statistically Speaking Login Programs Center Login All Logins

Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood by Karen

Two methods for dealing with missing data,vast improvements over traditional approaches, have become available in mainstream statistical software in the last few years. Both of the methods discussed here require that the data are missing at random–not related to the missing values. If this assumption holds, resulting estimates (i.e., regression coefficients and standard errors) will be unbiased with no loss of power. The first method is Multiple Imputation (MI). Just like the old-fashioned imputation methods, Multiple Imputation fills in estimates for the missing data. But to capture the uncertainty in those estimates, MI estimates the values multiple times. Because it uses an imputation method with error built in, the multiple estimates should be similar, but not identical. The result is multiple data sets with identical values for all of the non-missing values and slightly different values for the imputed values in each data set. The statistical analysis of interest, such as ANOVA or logistic regression, is performed separately on each data set, and the results are then combined. Because of the variation in the imputed values, there should also be variation in the parameter estimates, leading to appropriate estimates of standard errors and appropriate p-values. Multiple Imputation is available in SAS, S-Plus, R, and now SPSS 17.0 (but you need the Missing Values Analysis add-on module). The second method is to analyze the full, incomplete data set using maximum likelihood estimation. This method does not impute any data, but rather uses each cases available data to compute maximum likelihood estimates. The maximum likelihood estimate of a parameter is the value of the parameter that is most likely to have resulted in the observed data. When data are missing, we can factor the likelihood function. The likelihood is computed separately for those cases with complete data on some variables and those with complete data on all variables. These two likelihoods are then maximized together to find the estimates. Like multiple imputation, this method gives unbiased parameter estimates and standard errors. One advantage is that it does not require the careful selection of variables used to impute values that Multiple Imputation requires. It is, however, limited to linear models. Analysis of the full, incomplete data set using maximum likelihood estimation is available in AMOS. AMOS is a structural equation modeling package, but it can run multiple linear regression models. AMOS is easy to use and is now integrated into SPSS, but it will not produce residual plots, influence statistics, and other typical output from regression packages. References: Schafer, J. Software for Multiple Imputation Hox, J.J. (1999) A Review of Current Software for Handling Missing Data, Kwantitatieve Methoden, 62, 123-138. Allison, P. (2000). Multiple Imputation for Missing Data: A Cautionary Tale, Sociological Methods and Research, 28, 301-309. Tagged as: maximum likelihood, Missing Data, Mulitple Imputation, R, S-Plus, SAS, SPSS

Related Posts SPSS, SAS, R, Stata, JMP? Choosing a Statistical Software Package or Two. Multiple Imputation in a Nutshell Tricks for Using Word to Make Statistical Syntax Easier Ten Ways Learning a Statistical Software Package is Like Learning a New Language { 15 comments… read them below or add one }

Lau Hi Karen, I have the same problem as LF. I’m doing an Exploratory Factor Analysis and just 27 of all 198 participants completed every item. So I did a multiple imputation. But now I’m struggeling how to run the factor analysis. For any advice I would be very very thankful!! Reply

hosein hello – i am working in mineral exploration field -Do Cohen likelihood maximum Method for censored (missing) data replacement use for Geochemical data Now? Reply

Emily Stone Hello! I am doing Asymptotically distribution free estimation in AMOS due to a data set that is not normal and has ordinal data. I am trying to determine how to handle missing data with this type of estimation in AMOS. Can you do multiple imputation in AMOS? Thank you so much! Reply

Karen Hi Emily, AMOS doesn’t do multiple imputation, but you don’t need it to. It does maximum likelihood. You might find this helpful, though it’s not exactly what you’re doing: How to Use Full Information Maximum Likelihood in AMOS to Analyze Regression Models with Missing Data Reply

LF Thanks Karen. Any help to the above question about the difference in MPlus and AMOS is much appreciated. I am struggling with dealing with missing data and doing an Exploratory Factor Analysis with a complete dataset. I thought perhaps I could do Multiple Imputation in SPSS and do the EFA there but I don’t think it is one of the supported analyses for pooled data. Any suggestions how to use MI in an EFA in SPSS or do I have to switch to another software? Any help is much appreciated. Thank you. Reply

LF Hello Karen, In AMOS, when you use ML estimation with missing data, it says that the full sample is used. I’ve recently tried using MPlus and when it runs there, it says it takes out those cases from the analysis that doesn’t have any data on those variables. If it’s the same estimation method for missing data between the two packages, then why would it come out different. Is AMOS doing the same just not telling us it’s based on part of the sample? Thank you. Reply

Karen Hi LF, I don’t know MPlus, so I’m not sure what it is doing. AMOS isn’t dropping cases for having some missing data. I would suggest looking into the defaults in MPlus. Perhaps you just need to change an option. Any Mplus users want to chime in? Reply

Patrick Onyeneho How do i implement the add on of missing data using the .ML method in spss Thank you Reply

Michael Thanks Karen for the R free resource website.. Hi Peng, If you are looking for some case studies in R with real world proven examples you can try for some free classes at http://my-classes.com/ there are practice tests also available to self assess your knowledge. Reply

kaushal Chaudhary Hi Karen, SAS also used ml (maximum likelihood) or reml (restricted maximum likelihood) method for parameter estimation. Does this mean it also impute missing values in the data? So, if there are missing observartions, we do not have to impute. Thanks for your clarification. Reply

Karen Hi Kaushal, ML isn’t imputing. But yes, you can use SAS proc calis for missing predictors in a linear model or proc mixed for missing outcome values in a multilevel model. Reply

Dong I am looking into how to run an MLE. Can SPSS 20 run an MLE in it’s easy-to-use pull-down menus or can this only be done via syntax? Thank you! Reply

Karen It can. It is based on the analysis, however. What kind of model are you looking for? Reply

peng hi friends, I am new to R.I would like to know R-PLUS.Does any know where can I get the free training for R-PLUS. Regards, Peng. Reply

Karen Hi Peng, If you need free, I would suggest: http://www.ats.ucla.edu/stat/r/ Karen Reply Leave a Comment Name * E-mail * Website

Please note that, due to the large number of comments submitted, any comments on problems related to a personal study/project will not be answered. We suggest joining Statistically Speaking, where you have access to answers and more resources 24/7.

Submit { 1 trackback } Implications of Maximum Likelihood Methods for Missing Data in Predictive Modeling Applications | Dr. Ghulam Mohey-ud-din, PhD Previous post: Factor Analysis: A Short Introduction, Part 1 Next post: Factor Analysis: A Short Introduction, Part 2–Rotations

Statistically Speaking Webinar April 2018: Tests of Equivalence and Non-Inferiority

Upcoming Workshops Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models Logistic Regression: Binary, Ordinal, and Multinomial Variables

Free Webinars Generalized Linear Mixed Models

Search To search, type and hit enter

Read Our Book

Data Analysis with SPSS (4th Edition) by Stephen Sweet and Karen Grace-Martin

Statistical Resources by Topic Analysis of Variance and Covariance Books Complex Surveys & Sampling Count Regression Models Effect Size Statistics, Power, and Sample Size Calculations Linear Regression Logistic Regression Missing Data Mixed and Multilevel Models R SPSS Stata Copyright © 2008-2018 The Analysis Factor. All rights reserved. 877-272-8096 Contact Us

WordPress Admin

Smile Life

When life gives you a hundred reasons to cry, show life that you have a thousand reasons to smile

Get in touch

© Copyright 2015 - 2024 PDFFOX.COM - All rights reserved.