1 Non-Response Bias Analyses of the Survey of Workplace Violence Prevention Andrew Kato, Kathy Downey, William McCarthy, and Samantha Cruz U.S. Bureau of Labor Statistics The opinions expressed here are those of the authors and do not represent official policy of the Bureau of Labor Statistics.

2 Survey of Workplace Violence Prevention Special 2005 study conducted for NIOSH (National Institute for Occupational Safety and Health) Workplace Violence Prevention (WVP) Prevalence of security features, The risks facing employees, Employer policies and training, and Related topics associated with maintaining a safe work environment

3 WVP: Sampling Sample taken from respondents to 2003 SOII (Survey of Occupational Injuries and Illnesses) SOII - Private industry, State and local government public sector units from the fourth Quarter 2003 Longitudinal Database (LDB) and mining and railroad establishments Total of 39,998 units Randomly selected units proportional to size and oversampled within specific industries Used respondents so have prior relationship

4 WVP: Methodology Hardcopy was 12 pages, envelope and insert; also available to non-respondents in Word via Voluntary Protocol Initial mailing to SOII respondent (Sept 05), Follow-up mailing to non-respondents, Address corrections for post office returns, and Telephone follow-ups to non-respondents Close-out June 06 Final response rate was 61%

5 Purpose of Non-Response Analyses OMB requirement Examine potential bias due to non- response since predicted response rate might be low

6 Predominant Approaches to Conducting Non-Response (Olson, 2006) Comparing characteristics with a benchmark survey Comparing frame information between respondents and non-respondents* Simulating statistics based on restricted protocol (level of effort analyses)* Mounting experiments to produce varying response rates across groups

7 Data Available for Non-Response Analyses 2003 SOII frame data Size class 1: : : : : Sector (industry) – combined private and public 2003 SOII Rate (per 10,000 FTE hours) of job transfer or restriction Rate (per 10,000 FTE hours) of days-away-from- work cases

8 Data Analyses Compare non-respondents and respondents on 2003 frame data and survey responses Level of effort analyses: response propensity models

9 Comparing WPV Respondents and Non-Respondents: Size Class, by % NR *p<.0001 for overall

10 Comparing WPV Respondents and Non-Respondents: Size Class, by Average Days Away from Work Rate *p<.003 for class 1, p<.0001 for class 3

11 Comparing WPV Respondents and Non- Respondents: Size Class, by Average Job Transfer or Restriction Rate *p<.0001 for classes 3, 4, and 5

12 Comparing WPV Respondents and Non-Respondents: Selected Sectors, by % NR *p<.0001 for selected sectors above

13 Comparing WPV Respondents and Non- Respondents: Selected Sectors, by Average Days Away from Work Rate *p<.02 transport, p<.004 rest

14 Comparing WPV Respondents and Non- Respondents: Selected Sectors, by Average Job Transfer or Restriction Rate *varying significant p values

15 Comparing WPV Respondents and Non-Respondents: Conclusions Dealing with four projects when discussing: 2003 SOII sample and analyzed 2003 SOII estimates of DAW and JTR (injuries/illness) WPV survey – existence of violence prevention programs This NR project 2001 one-time Respirator Survey – used 1999 SOII sample

16 Comparing WPV Respondents and Non-Respondents: Conclusions Non-respondents versus respondents Higher size class showed curvilinear effect with size 3 highest Industry had some variation possibly those with more public units

17 Comparing WPV Respondents and Non-Respondents: Conclusions *p<.0001

18 Comparing WPV Respondents and Non-Respondents: Conclusions – Days Away from Work, Job Transfer Higher rates are responding more Size Days away from work rate: highest class 3, lowest 1 Job transfer or restriction rate: highest 3, 4, 5 Industry Days away from work rate: wholesale, retail, real estate, health care, transportation Job transfer or restriction rate: same plus public admin, admin support, manufacturing

19 Level of Effort: Response Propensity Models for Contact and Cooperation Predicting Contact Predicting Cooperation CoeffSECoeffSE Intercept5.09** **0.21 Size Size Size *0.06 Size Hours (FTE worked)0.00 DART case rate* * st NR mail – Nov * st round calling ** nd NR mail – Apr ** ** nd round calling **0.15 *p<.05 **p<.0001

20 Level of Effort: Response Propensity Strata for Contact and Cooperation Response Propensity Strata LowGrp 2Grp 3Grp 4High Contact Actual rate (n) 51% (5021) 55% (5021) 88% (5021) 99% (5021) 97% (5021) Est. Non- contacts 51%54%58%99% Est. Contacts 51%54%92%99%100% Cooperation Actual rate 36% (1410) 55% (2179) 98% (3883) 99% (3921) 100% (3935) Est. Ref.32%55%98%100% Est. Coop43%56%99% 100%

21 Conclusions Some differences between WPV respondents and non-respondents Size Industry SOII estimates Respondents to WPV have more programs and more SOII incidents Not sure what impact to WPV, possibly respondents have more programs and more risks (over-reporting?)

22 Limitations Limitation: only as good as phone logs from vendor (contact/non- contact) Learned over surveys to not have subcontractors so can have more detail in phone logs, manage mailings/contacts

23 Future Research Level of effort analyses tied into data estimates How data might change for key WPV estimates at different levels of effort (truncation) Huge work to re-weight, though More WPV analyses – 3-digit NAICS, like transportation