BIFIEsurvey-package.RdContains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>, <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, <doi:10.1080/02331889708802523>). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria).
The BIFIEsurvey package include basic descriptive functions for large scale assessment data to complement the more comprehensive survey package. The functions in this package were written in Rcpp.
The features of BIFIEsurvey include for designs with replicate weights (which includes Jackknife and Bootstrap as general approaches):
Descriptive statistics: means and standard deviations (BIFIE.univar),
frequencies (BIFIE.freq),
crosstabs (BIFIE.crosstab)
Linear regression (BIFIE.linreg)
Logistic regression (BIFIE.logistreg)
Path models with measurement error correction for manifest
variables (BIFIE.pathmodel)
Two-level regression for hierarchical data (BIFIE.twolevelreg;
random slope model)
Statistical inference for derived parameters (BIFIE.derivedParameters)
Wald tests (BIFIE.waldtest) of model parameters based on
replicated statistics
User-defined R functions (BIFIE.by)
Bruneforth, M., Oberwimmer, K., & Robitzsch, A. (2016). Reporting und Analysen. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 333-362). Wien: facultas.
George, A. C., Oberwimmer, K., & Itzlinger-Bruneforth, U. (2016). Stichprobenziehung. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 51-81). Wien: facultas.
Kolenikov, S. (2010). Resampling variance estimation for complex survey data. Stata Journal, 10(2), 165-199.
Pfefferman, D., & Rao, C. R. (2009a). Handbook of statistics, Vol. 29A: Sample surveys: Design, methods and applications. Amsterdam: North Holland.
Pfefferman, D., & Rao, C. R. (2009b). Handbook of statistics, Vol. 29B: Sample surveys: Inference and analysis. Amsterdam: North Holland.
Robitzsch, A., Pham, G., & Yanagida, T. (2016). Fehlende Daten und Plausible Values. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 259-293). Wien: facultas.
Shao, J. (1996). Invited discussion paper: Resampling methods in sample surveys. Statistics, 27(3-4), 203-237.
See also the survey, intsvy, EdSurvey, lavaan.survey, EVER and the eatRep packages.
## |-----------------------------------------------------------------
## | BIFIEsurvey 0.1-21 (2014-06-21)
## | Maintainer: Alexander Robitzsch <a.robitzsch at bifie.at >
## | http://www.bifie.at
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