1. Exploring Demographic and Employment Characteristics of Employees with Self-reported Gambling Problems Margaret K. Glenn, EdD, CRC ; Carolyn E. Hawley, Ph.D., CRC West Virginia University West Virginia University I: Using Exploratory Data Analyses in Addition to Conventional Null-Hypothesis-Testing (NHT) Approaches to Explore Variables Influencing Employment Status Chi-squared Automatic Interaction (CHAID) Technique analysis: CHAID is a non-parametric analysis based on statistically recursive partitioning algorithms. The CHAID Technique determines the relative importance of each of the independent (predictor) variables in explaining group membership in a categorical dependent (outcome) variable. CHAID* Analysis helps investigators “re-think” how ordinal response ranges are constructed. Here we see that when predicting the employment status of people seeking help for problem gambling, “Age” is the most significant factor. For people ages < 36, and 36- 55, “Co-morbidity” is the next most significant factor with individual’s reporting pre-existing SA problems more likely to be employed than those reporting MH conditions. However, for individuals with a pre-existing Mental Health condition < 36, “Life Event” (defined as an event that precipitated gambling and is believed by the individual to have triggered the behavior), was the next most significant factor, while for ages 36-55, “Family History” is significant. Individuals in this category with a family history of PG or SA are less likely to be employed. Finally, for individuals 55-64, women are more likely to be employed than men. * The intention of this analysis is to determine factors that place people at risk for developing gambling problems in order to develop targeted workplace prevention and intervention programs. II: Occupational Categories of Individuals Seeking Help for Problem Gambling (PG) Dendograms (i.e., classification trees) are utilized to display the relative importance of statistically significant independent variables on the dependent variable. The hierarchical nature of the CHAID dendograms provide a visual depiction of variable interactions that may not be otherwise observable or detected in traditional analytic procedures. March 29, 2008 * 2008 Annual American Counseling Association Conference, Hawaii

2. CHAID* Analysis helps investigators “re-think” how ordinal response ranges are constructed. Here we see that when predicting the employment status of people seeking help for problem gambling, “Age” is the most significant factor. For people < 36, and 36- 55 “Co- morbidity” is the next most significant factor with individual’s reporting pre-existing SA problems more likely to be employed than those reporting MH conditions. However, for individuals with a pre-existing Mental Health condition < 36, “Life Event” (defined as an event that precipitated gambling and is believed by the individual to have triggered the behavior), was the next most significant factor; while for ages 36-55, “Family History” is significant. Individuals with a family history of PG or SA are less likely to be employed.

3. Standard Occupation (SOC) Category SOC # N%Occupation/ Employment Setting N Managemen tBusiness, Financial 11- 13 589.3Management36 Finance/Insurance17 Executive5 Professional15-2914122.5Social Service66 Education29 Computer/Informati on Technology 19 Healthcare15 Professional/Scienti fic/Technology 10 Arts/Entertainment2 Service31-3910316.4Food/Hotel78 Gaming13 Law Enforcement8 Administrative Waste Management 4 Sales and Office 41-4313721.9Sales47 Clerical35 Retail Clerk28 Postal Employee13 Wholesale/Retail Trade 10 Real Estate, Rental and Leasing 4 Natural Resources, Constructio nProduction, Transportati on 45-5314923.8Transportation/War ehousing 33 Manufacturing35 Construction29 Laborer20 Industrial23 Mining9 Military Government 55-60396.2State Government Employee 27 Federal Government Employee 10 Military2 Total685100.0 68 5

5. Category%n Employed66713 Unemployed33359 Total(100)1072 Node 0 Category%n Employed4463 Unemployed5679 Total(13)142 Node 3 Category%n Employed3322 Unemployed6744 Total(6)66 Node 9 Category%n Employed5441 Unemployed4635 Total(7)76 Node 8 Category%n Employed62203 Unemployed38122 Total(30)325 Node 2 Category%n Employed5386 Unemployed4776 Total(15)162 Node 7 Category%n Employed72117 Unemployed2846 Total(15)163 Node 6 Category%n Employed6261 Unemployed3837 Total(9)98 Node 13 Category%n Employed8656 Unemployed149 Total(6)65 Node 12 Category%n Employed74447 Unemployed26158 Total(56)605 Node 1 Category%n Employed68157 Unemployed3273 Total(21)230 Node 5 Category%n Employed5760 Unemployed4345 Total(10)105 Node 11 Category%n Employed7897 Unemployed2228 Total(12)125 Node 10 Category%n Employed77290 Unemployed2385 Total(35)375 Node 4 Employment Status Current Age Adj. P-value=0.0000, Chi-square=48.4229, df=2 56-64 Gender Adj. P-value=0.0137, Chi-square=6.0814, df=1 MaleFemale 36-55 Co-Morbidity Adj. P-value=0.0015, Chi-square=12.1081, df=1 Mental Health ConditionSubstance Abuse, Family History Adj. P-value=0.0027, Chi-square=11.0282, df=1 Family HX of Addiction; Family HX of Gambling 18-35 Co-Morbidity Adj. P-value=0.0410, Chi-square=6.0814, df=1 Mental Health Condition Triggering Life Events Adj. P-value=0.0279, Chi-square=11.0229, df=1 HealthEmployment; Other; Death; Relationship Substance Abuse