1. Agresti‎, ‎A‎. ‎(2002)‎. ‎Categorical Data Analysis‎. ‎New York‎, ‎John Wiley & Sons‎. [DOI:10.1002/0471249688]

2. Becker‎, ‎G‎. ‎S‎. ‎(1991)‎. A Treatise on the Family‎. ‎Cambridge‎, ‎Harvard University Press‎.

3. Buonaccorsi‎, ‎J.P‎. ‎(2010)‎. ‎Measurement Error‎, ‎Models‎, ‎Methods and Applications‎. ‎New York‎, ‎CRC Press‎.

4. ‎Buonaccorsi‎, ‎J.P.‎, ‎Romeo‎, ‎G.‎, ‎and Thoresen‎, ‎M‎. ‎(2018)‎. ‎Model-Based Bootstapping when Correcting for Measurement Error with Application to Logistic Regression‎. Biometrics‎, 74‎, ‎135-144‎. [DOI:10.1111/biom.12730]

5. Carroll‎, ‎R.J.‎, ‎Ruppert‎, ‎D.‎, ‎Stefanski‎, ‎L.A.‎, ‎and Crainiceanu‎, ‎C.M‎. ‎(2006)‎. ‎Measurement Error in Nonlinear Models‎: ‎A Modern Perspective‎. ‎Boca Raton‎, ‎CRC Press‎.

6. Cheng‎, ‎D.‎, ‎Branscum‎, ‎A.J‎. ‎and Stamey‎, ‎J.D‎. ‎(2010)‎. ‎Accounting for Response Misclassification and Covariate Measurement Error Improves Power and Reduces Bias in Epidemiologic Studies‎. ‎Annals of Epidemiology‎, 20‎, ‎562-567‎. [DOI:10.1016/j.annepidem.2010.03.012]

7. DeGraff‎, ‎D‎. ‎S‎. ‎(1991)‎. ‎Increasing Contraceptive Use in Bangladesh‎: ‎The Role of Demand and Supply Factors‎. Demography‎, 28‎, ‎65-81‎. [DOI:10.2307/2061336]

8. Diggle‎, ‎P.J.‎, ‎Liang‎, ‎K.Y.‎, ‎and Zeger‎, ‎S.L‎. ‎(1994)‎. ‎Analysis of Longitudinal Data‎. ‎Oxford‎, ‎Oxford University Press‎.

9. Gerlach‎, ‎R‎. ‎and Stamey‎, ‎J‎. ‎(2007)‎. ‎Bayesian Model Selection for Logistic Regression with Misclassified Outcomes‎. Statistical Modelling‎, 7(3)‎, ‎255-273‎. [DOI:10.1177/1471082X0700700303]

10. Goldstein‎, ‎H‎. ‎(2011)‎. ‎Multilevel Statistical Models (4th ed.)‎. ‎London‎, ‎John Wiley & Sons‎.

11. Hossain‎, ‎M.B.‎, ‎(2005)‎. ‎Analysing the Relationship between Family Planning Workers' Contact and Contraceptive Switching in Rural Bangladesh Using Multilevel Modelling‎. ‎Journal of Biosocial Science‎, ‎37‎, ‎529-544‎. [DOI:10.1017/S0021932004007096]

12. Huq‎, ‎N.M‎. ‎and Cleland‎, ‎J‎. ‎(1990)‎. ‎Bangladesh Fertility Survey‎, ‎National Institute of Population Research and Training (NIPORT)‎.

13. Jaeckel‎, ‎P‎. ‎(2005)‎. ‎A Note on Multivariate Gauss-Hermite Quadrature‎. ‎London‎: ‎ABN-Amro‎. ‎Retrieved‎

14. ‎from ‎http://www.pjaeckel.webspace.virginmedia.com/ANoteOnMultivariateGaussHermiteQuadrature.pdf‎

15. Johnston‎, ‎H‎. ‎(1999)‎. ‎Measuring Induced Abortion‎: ‎Integrating Qualitative with Quantitative‎ ‎Research Techniques‎. ‎Ph.D‎. ‎thesis‎: ‎The Johns Hopkins University‎.

16. Lapham‎, ‎R‎. ‎J‎. ‎and Mauldin‎, ‎W‎. ‎P‎. ‎(1984)‎. ‎Family Planning Effort and Birthrate Decline in Developing Countries‎, ‎International Family Planning Perspectives‎. 10‎, ‎109-l18‎. [DOI:10.2307/2948060]

17. Lele‎, ‎S.R.‎, ‎Dennis‎, ‎B.‎, ‎and Lutscher‎, ‎F‎. ‎(2007)‎. ‎Data Cloning‎: ‎Easy Maximum Likelihood Estimation for Complex Ecological Models Using Bayesian Marcov Chain Monte Carlo Methods‎. Ecology Letters‎. 10‎, ‎551-563‎. [DOI:10.1111/j.1461-0248.2007.01047.x]

18. Lyles‎, ‎R.H.‎, ‎Tang‎, ‎L.‎, ‎Superak‎, ‎H.M.‎, ‎King‎, ‎C.C.‎, ‎Celentano‎, ‎D.D.‎, ‎Lo‎, ‎Y‎. ‎and Sobel‎, ‎J.D‎. ‎(2011)‎. ‎Validation Data-Based Adjustments for Outcome Misclassification in Logistic Regression‎: ‎an Illustration‎. Epidemiology (Cambridge‎, ‎Mass.)‎, 22‎, ‎589‎. [DOI:10.1097/EDE.0b013e3182117c85]

19. Magder‎, ‎L.S‎. ‎and Hughes‎, ‎J.P‎. ‎(1997)‎. ‎Logistic Regression when the Outcome Is Measured with Uncertainty‎. American Journal of Epidemiology‎, 146‎, ‎195-203‎. [DOI:10.1093/oxfordjournals.aje.a009251]

20. McCullach‎, ‎C.E‎. ‎(1994)‎. ‎Maximum Likelihood Variance Components Estimation for Binary Data‎. Journal of the American Statistical Association‎, 89‎, ‎330-335‎. [DOI:10.1080/01621459.1994.10476474]

21. McCullach‎, ‎C.E.‎, ‎Searle‎, ‎S.R.‎, ‎and Neuhaus‎, ‎J.M‎. ‎(2008)‎. ‎Generalized‎, ‎Linear‎, ‎and Mixed Models (2nd ed.)‎. ‎London‎, ‎John Wiley & Sons‎.

22. Mitra‎, ‎S.N‎. ‎and Al-Sabir‎, ‎A‎. ‎(1996)‎. ‎DHS Working Papers‎.

23. Molenberghs‎, ‎G.‎, ‎and Verbeke‎, ‎G‎. ‎(2006)‎. ‎Models for Discrete Longitudinal Data‎. ‎New York‎, ‎Springer Science & Business Media‎.

24. Nelder‎, ‎J‎. ‎A‎. ‎and Mead‎, ‎R‎. ‎(1965)‎. ‎A Simplex Algorithm for Function Minimization‎. Computer Journal‎, 7‎, ‎308-–313‎. [DOI:10.1093/comjnl/7.4.308]

25. Neuhaus‎, ‎J.M‎. ‎(1999)‎. ‎Bias and Efficiency Loss Due to Misclassified Responses in Binary Regression‎. Biometrika‎, 86(4)‎, ‎843-855‎. [DOI:10.1093/biomet/86.4.843]

26. Neuhaus‎, ‎J.M‎. ‎(2002)‎. ‎Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification‎. Biometrics‎, 58(3)‎, ‎675-683‎. [DOI:10.1111/j.0006-341X.2002.00675.x]

27. Pan‎, ‎J‎. ‎and Thompson‎, ‎R‎. ‎(2003)‎. ‎Gauss-Hermite Quadrature Approximation Estimation in Generalized Linear Mixed Models‎. Computational Statistics‎, 18‎, ‎57-78‎. [DOI:10.1007/s001800300132]

28. Paulino‎, ‎C‎. ‎D.‎, ‎Soares‎, ‎P‎. ‎and Neuhaus‎, ‎J‎. ‎(2003)‎. ‎Binomial Regression with Misclassification‎. Biometrics‎, 59‎, ‎670-675‎. [DOI:10.1111/1541-0420.00077]

29. R Core Team‎. ‎R (2019)‎. ‎A Language and Environment for Statistical Computing‎. ‎R Foundation for Statistical Computing‎. ‎Vienna‎, ‎Austria‎.

30. Rasbash‎, ‎J.‎, ‎Steele‎, ‎F.‎, ‎Browne‎, ‎W‎. ‎J.‎, & Goldstein‎, ‎H‎. ‎(2009)‎. A User's Guide to MLwiN‎, ‎v2‎. ‎10‎. ‎London‎: ‎Institute of Education‎.

31. Roy‎, ‎S‎. ‎(2012)‎. ‎Accounting for Response Misclassification and Covariate Measurement Error Using a Random Effect Logit Model‎. Communications in Statistics-Simulation and Computation‎, 41‎, ‎1623-1636‎. [DOI:10.1080/03610918.2011.611312]

32. Skrondal‎, ‎A‎. ‎and Rabe-Hesketh‎, ‎S‎. ‎(2004)‎. Generalized Latent Variable Modeling‎. ‎Boca Raton‎, ‎CRC Press‎. [DOI:10.1201/9780203489437]

33. Stroup‎, ‎W.W‎. ‎(2012)‎. Generalized Linear Mixed Models‎: ‎Modern Concepts‎, ‎Methods and Applications‎. ‎Boca Raton‎, ‎CRC Press‎.

34. Tang‎, ‎L.‎, ‎Lyles‎, ‎R.H.‎, ‎King‎, ‎C.C.‎, ‎Hogan‎, ‎J.W‎. ‎and Lo‎, ‎Y‎. ‎(2015)‎. ‎Regression Analysis for Differentially Misclassified Correlated Binary Outcomes‎. Journal of the Royal Statistical Society‎. ‎Series C‎, ‎Applied Statistics‎, 64‎, ‎433‎. [DOI:10.1111/rssc.12081]

35. Tanner‎, ‎M.A‎. ‎(1993)‎. Tools for Statistical inference‎: ‎Observed Data and Data Augmentation(2nd ed.)‎. ‎New York‎, ‎Springer Science & Business Media‎.

36. Torabi‎, ‎M‎. ‎(2013)‎. ‎Likelihood Inference in Generalized Linear Mixed Measurement Error Models‎. Computational Statistics and Data Analysis‎, 57‎, ‎549-557‎. [DOI:10.1016/j.csda.2012.07.018]

37. Wang‎, ‎N.‎, ‎Lin‎, ‎X.‎, ‎and Guttierrez‎, ‎R.G‎. ‎(1999)‎. ‎A Bias Correction Regression Calibration Approach in Generalized Linear Mixed Measurement Error Models‎. Communications in Statistics‎, 28‎, ‎217-232‎. [DOI:10.1080/03610929908832292]

38. Wang‎, ‎N.‎, ‎Lin‎, ‎X.‎, ‎Guttierrez‎, ‎R.G.‎, ‎and Carroll‎, ‎R.J‎. ‎(1998)‎. ‎Bias Analysis and SIMEX Approach in Generalized Linear Mixed Measurement Error Models‎, Journal of American Statistical Association‎, 93‎, ‎249-261‎. [DOI:10.1080/01621459.1998.10474106]

39. Wu‎, ‎L‎. ‎(2009)‎. Mixed Effects Models for Complex Data‎. ‎Boca Raton‎, ‎CRC Press‎.

40. Xie‎, ‎X.‎, ‎Xue‎, ‎X‎. ‎and Strickler‎, ‎H.D‎. ‎(2017)‎. ‎Generalized Linear Mixed Model for Binary Outcomes when Covariates are Subject to Measurement Errors and Detection Limits‎. Statistics in Medicine‎, 37‎, ‎119-136‎. [DOI:10.1002/sim.7509]

41. Yi‎, ‎G.Y‎. ‎(2016)‎. Statistical Analysis with Measurement Error or Misclassification‎. ‎New York‎, ‎Springer‎.