Muhammad Imran Khan

1. Using Modern Missing Data Analyses for effective inference about Hunters’ satisfaction towards OFW Program Muhammad Imran Khan

2. Motivation of Study • Hunting & fishing are part of Nebraska's heritage • NGPC is interested in improving hunter/angler recruitment & retention (NGPC,2008) • Data collected in 2013 to know about hunters’ motivations & satisfactions towards OFW lands • Purpose of this study is to compare estimates using appropriate imputation methods

3. Missing Data • Missingness in Surveys (Groves et al., 2004) • Noncoverage • Unit Nonresponse • Item Nonresponse • Partial Nonresponse (Brick & Kalton,1996) • Data Entry Error (Anne & Andrea,2014) • Missing data Mechanism(Buuren, 2012) • Missing Completely At Random (MCAR) • Missing At Random (MAR) • Missing Not At Random (MNAR)

4. How much missing data is “problematic” • Researchers assign some limits: • > 5% (Schafer,1999) • >10% (Benntt,2001) • >20% (Peng et al., 2006) • (Widaman,2006) specified the following scale • 1%-2% (Negligible) • 5%-10% ( Minor) • 10%-25% (Moderate) • 25%-50% (High) • >50% (Excessive) • Important problems of missingness (Bell & Fairclough,2013) • decrease in precision • Increase bias in parameter estimation

5. NGPC & UNL conducted survey • Sampling frame: hunters who purchased hunting license for hunting in 2012 in NE • The survey contained three parts: • Where, & what hunt; Environment Impact • Motivations(Relatedness, Competence, Autonomy) • Socio-demographic factors • About collected data • Total questions = 42 (used 19 Qus. for analysis) • Sample size = 8181 • Completely filled =1555 (19%) • Unit nonresponse = 627 (8%) • Item nonresponse = 5999 (73%) • Varies from 1 to 8 missingness per respondent in all 19 Qus. 81%

6. Determining Type of Missing Data • Test for MCAR (Little, 1988) • Little’s Test of MCAR (Omnibus test of all specified variables) • If test is not significant, then data can be assumed MCAR • If test is significant, then Then, data may be MAR or MNAR • For given data test is sig. So data are MAR • 3256.783 with . • Table shows number & percent missing

7. Data used for analysis • 13 Questions for motivation based on SDT 5 Questions on relatedness transformed to 2 factors

8. Data used for analysis • 13 Questions for motivation based on SDT 4 Qus. on competence & autonomy transformed each to 1 factor

9. Model used for the analysis Satisfaction=Rel_1+Rel_2+Comp+Auto+ Educ+Age+Income+H_Days+Harvest

10. Methods for Handling Missing Data • Deletion or non-imputing methods: • List-wise Deletion (Pigott, 2001) • Pair-wise Deletion (Bennett, 2001) • Nonstochastic or ad-hoc methods: • Mean Imputation (Graham,2003) • Regression Imputation (Qin et.al., 2007) • Stochastic or Establishedmethods: • Stochastic Regression (Todd et al., 2013) • Multiple Imputation(MI) (John, et al., 2007) • Full Information Maximum Likelihood(FIML) • Expectation Maximization (EM)(Yiran& Chao-Ying, 2013)

11. Mean Imputation

12. Comparing Results

13. Multiple Imputation

14. Comparing Results

15. Comparing Results

16. Summary • EM only shows that Releadness_2 is significant • EM estimates smallest standard error for Income • Comparison of Imputation Methods

17. Thanks for your kind attention Special Thanks to: Dr. Andrew Tyre, Uni. Of Nebraska, Lincoln Dr. Lisa Pennisi, Uni. Of Nebraska, Lincoln Dr. Allan McCutcheon, Uni. Of Nebraska, Lincoln Nebraska Game & Parks Commission

18. References Anne-Kathrin,F. & Andrea B. (2014). The economic performance of Swiss drinking water utilities. Journal of Prod. Analysis. 41:383-397. doi10.1007/s11123-013-0344-0 Bell, M. L.,& Fairclough,D.L. (2013). Practical and statistical issues in missing data for longitudinal patient reported outcomes. Statistical Methods in Medical Research, 0(0), 1-20. doi: 10.1177/0962280213476378 Bennett, D.A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25, 464-469. Brick, J., & Kalton, J. (1996). Handling missing data in survey research. Statistical Methods in Medical Research, 5, 215–238. doi:10.1177/096228029600500302 Buuren, S.V.(2012). Flexible imputation of missing data. Taylor & Francis, FL: CRC Press. John, W. G. & Allison E. O. & Tamika D. G.(2007). How many imputations are really needed? some practical clarifications of multiple imputation theory, Springer,8:206- 213. Graham, J. W. (2003). Adding missing-data-relevant variables to FIML based structuralequationmodels. Structural Equation Modeling, 10,80–100. Groves, R., Fowler, F., Couper, M., Lepkowski, J., Singer, E., & Tourangeau, R. (2004). Survey methodology. Hoboken, NJ: John Wiley. Little, R.J.A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association , 83, 1198-1202. NGPC (2008). Nebraska 20 year hunter/angler recruitment, development and retention plan. Lincoln, NE. Pigott, T. D. (2001). A Review of Methods for Missing Data. Educational Research and Evaluation, 7(4), 353-383. Peng, C.Y., Harwell, M., Liou, S.M., & Ehman, L.H. (2006). Advances in missing data methods and implications for educational research. In S Sawilowsky (Ed.), Real data analysis (pp.31-78), Greenwich, CT: Information Age. Qin,Y.,Zhang,S.,Zhu,X.,Zang,J.,& Zhang,C. (2007). Semi-parametric optimization for missing data imputation. ApplIntell 27,79-88. DOI 10.1007/s10489-006-0032-0 Schafer, J.L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research. 8: 3-15. Todd D. L., Terrence D. J., Kyle M. L., & Whitney M. (2013). On the joys of missing data. Journal of Pediatric Psychology, 1-12. doi:10.1093/jpepsy/jst048 Yiran D. & Chao-Ying J.P.(2013). Principled missing data methods for researchers. Springer, 2:222.

19. Questions & Comments! are most welcome Contact Information: mik3.stat@gmail.com