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Using Modern Missing Data Analyses for effective inference about Hunters’ satisfaction towards OFW Program. Muhammad Imran Khan. Motivation of Study. Hunting & fishing are part of Nebraska's heritage NGPC is interested in improving hunter/angler r ecruitment & retention ( NGPC,2008 )

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Muhammad imran khan

Using Modern Missing Data Analyses for effective inference about Hunters’ satisfaction towards OFW Program

Muhammad Imran Khan


Motivation of study

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


Missing data

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)


How much missing data is problematic

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


Ngpc unl conducted survey

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%


Determining type of missing data

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


Data used for analysis

Data used for analysis

  • 13 Questions for motivation based on SDT

    5 Questions on relatedness transformed to 2 factors


Data used for analysis1

Data used for analysis

  • 13 Questions for motivation based on SDT

    4 Qus. on competence & autonomy transformed each to 1 factor


Model used for the analysis

Model used for the analysis

Satisfaction=Rel_1+Rel_2+Comp+Auto+

Educ+Age+Income+H_Days+Harvest


Methods for handling missing data

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)


Mean imputation

Mean Imputation


Comparing results

Comparing Results


Multiple imputation

Multiple Imputation


Comparing results1

Comparing Results


Comparing results2

Comparing Results


Summary

Summary

  • EM only shows that Releadness_2 is significant

  • EM estimates smallest standard error for Income

  • Comparison of Imputation Methods


Thanks for your kind attention

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


References

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.


Muhammad imran khan

Questions & Comments!

are most welcome

Contact Information: [email protected]


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