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Adjusting for Unequal Selection Probability in Multilevel Models:
A listing of Software Programs Kim Chantala Chirayath Suchindran Carolina Population Center, UNC at Chapel Hill Abstract Most surveys collect information with complicated sampling Plans that involve selecting both clusters and Individuals with unequal probability of selection. Research in using multilevel modeling Methods to Analyze this data is relatively brand new. Often sampling Weights based on probabilities of selecting Individuals are used to gauge population-based models. But, sampling weights used for Constructed differently than weights used for population-average models. This paper compares the Capacities of MLWIN, MPLUS, LISREL, PROC MIXED (SAS), and gllamm (Stata) for estimating MLM using data gathered with a complex sampling plan. We exemplify how sampling weights need to be Assembled for estimating MLM with these software Packages. Finally, we contrast the outcomes in those Packs using information collected using a complex Sampling plan.
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Details: multilevel models, sample weights, Applications Inch. Introduction Population polls commonly utilize complicated sampling Intends to get information about individuals. These Sampling plans frequently demand sampling both clusters And individuals with unequal probability of selection. Special evaluation techniques are Required to calculate Assessing data accumulated with these processes. The Purpose of the document is to demonstrate the employment of Recent improvements in statistical procedures to easily adjust For the sampling design faculties by using Appropriate sampling weights to gauge multilevel models. Extensive research in Assessing single-level (population-average or marginal) models from Complex survey data has resulted in the accessibility Of many applications programs (SUDAAN, svy Controls in Stata, also SURVEYFREQ, SURVEYREG etc. in SAS) that use proper Design-based analysis techniques for complex survey data. Yet, research in evaluation methods for Estimating multilevel models from complex survey Data is quite recent (Pfefferman (1998), Stapleton (2002), Asparouhov (2006)). Not only does this Research led to new methods for incorporating Sampling weights into multicolored models, but has Highlighted an important thing often overlooked by Both analysts and providers of this survey data: the Sampling weights useful for multilevel analysis have to Be constructed otherwise compared to sampling weights Useful for single-level investigation. The sampling weight Used in estimating single-level units is computed as The reverse of the probability which the patient was Selected from the population and represents the Number of people in the population which can be Likely to answer the questionnaire in a way similar to the Individual consulted. Such a sampling weight Is dispersed with data from people surveys. Ideally, the estimation of those multilevel models Necessitates climbing weights at each degree of sampling. Public usage data might not supply this information. We first review available software packages for Estimating multilevel models that incorporate Sampling weights in investigation. Next, we discuss The sampling weights for multi level models need to Be assembled for all these packages. We conclude by Using data from the National Longitudinal Study of Adolescent Health (Add Health) to provide examples Of estimating a multilevel model with a few of these Bundles. 2. Some of the commercially available software Packages make it possible for analysts to use sampling weights When Assessing Structural Equation Models (SEM) As shown in Table 1, That the SEM software programs include M Plus, LISREL, and the Stata user written program gllamm. Aside from MIXED and NLMIXED from SAS, all of These packages have been designed to test data Collected with a complex sampling plan. ASA Section on Survey Research Techniques 2815 Table 1. General Details about Software Applications. SEM Diagnosis MLM Diagnosis Fix for Clustering Fix for Stratification Sub Population Diagnosis LISREL 8.8 • • • • GLLAMM (Stata 9) • • • MIXED (SAS 9.1) • • NLMIXED (SAS 9.1) • • Besides allowing sampling weights for Assessing single-level models, many of these Software packages also permit users to define Stretching weights created for estimating multilevel Models (table two). Because these weights need to be Assembled differently than sampling weights used To get single-level models, consumers should make certain that the Weights they're utilizing are scaled correctly for the Special computer software program being used for MLM Investigation. While MIXED (SAS 9.1) does permit users to specify One fat, the burden isn't expected to be a Sampling weight, however a burden designed to be observations. Thus, users should be rather careful in Using MIXED when assessing data accumulated with a Complex sampling plan. SAS also provides a Separate bundle, NLMIXED, for estimating Nonlinear multi level models. Although There's no Weight announcement available with NLMIXED, special Weighting procedures have been executed Through a SAS macro to correct to your sampling design (Grilli and Pratesi, 2004). Table 3 lists the types of MLM analyses available From these types of packages that allow users to use Multilevel sampling weights. The vendors of MPLUS, '' MLWIN, LISREL, and HLM report that the Newest versions of these applications bundles all Produce comparable results when estimating models From complex survey data. 3. Data used in cases Examples of this paper utilize data from the National Longitudinal Study of Adolescents (Add Health). Add Health is a longitudinal study of adolescents Recorded on grade 7-12 registration rosters for the 1994- And 52 middle schools had been chosen using unequal Probability of choice. Adding systematic Sampling methods and suggested stratification at the Study design assured that these schools were Representative of US schools concerning region of Country, location (urban, suburban, and rural), faculty Type (private, public, parochial), percentage of Students that were white , and school size. Administrators at each school have been asked to Submit a Table 2. Allow MLM Sampling Weights Method for Growing MLM Sampling Weights Responsibility for Growing MLM Sampling Weights LISREL 8.8 • Pfefferman, et al (1998) LISREL MLWIN 2.02 • Pfefferman, et al (1998) User or MLWIN MIXED (SAS 9.1) Not Known User NLMIXED (SAS 9.1) Grilli, along with Pratesi, (2004) User 2816 Table 3. Different types of Multilevel analysis that allow utilization of sampling weights during estimation. Outcome Variable Pc Software Program Normal Binary Poisson Multinomial Categorical Ordered Categorical LISREL 8.7 • GLLAMM (Stata 9) • • • • • MLWIN 2.02 • • • • • NLMIXED (SAS 8.2) • • Special poll that captured traits of this School. Add Health has accumulated four panels of Information On teens: In-School (1994), the Wave I Inhome Survey (1995), the Wave II In-home Survey (1996), and the Wave III In-home Survey (2001). The In-School survey contained all pupils from Sampled schools that were in attendance on the afternoon The survey was administered. The Wave I Had Survey selected students from the enrollment rosters Of those 132 schools together with unequal probability of selection. Several special over-sampled groups were Also recruited for the Wave I encounter. These Include the center sample (roughly equal-sized Supplements (Black adolescents whose parents were College graduates, teens whose race was Cuban, Puerto Rican, or Chinese.) , the disabled sample, and the genetic supplement (biologically Related teens, non-related adolescents living together). The Wave II and Wave III samples were Selected from the Wave I respondents. In addition to Providing sampling weights that have been developed for Schools and adolescents are available for each wave of the Add Health data. The descriptive statistics for These weight parts are shown in table. 4. Sampling Weights used in Multi Level Analysis If the likelihood of being selected is related to the Outcome variable despite conditioning the Model covariates, the sampling process is informative And it'll be important to adjust the quotes for the Sampling procedure. Some analysts Choose to correct for The sampling design with the addition of covariates to the Version rather than using sampling weights to correct For the sampling process. Because of the big Number of variables involved from the sampling Process, this may add unwanted complexity to the Version and interfere with all the scientific reasons for Conducting the diagnosis.
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