GET
  FILE='K:\[Emergency Medicine] Diagnostic Testing Research\Summer 2016 [Rahul & Chelsea]\Data\Rahul2016DiagnosticAccuracyUMHS.sav'.
DATASET NAME DataSet1 WINDOW=FRONT.
LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost
  /CONTRAST (TbiBenefit)=Indicator
  /CONTRAST (TbiRisk)=Indicator
  /CONTRAST (TbiCost)=Indicator
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

Logistic Regression

Notes	
Output Created	23-APR-2019 16:45:35	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Summer 2016 [Rahul & Chelsea]\Data\Rahul2016DiagnosticAccuracyUMHS.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	1017	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing	
Syntax	LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost
  /CONTRAST (TbiBenefit)=Indicator
  /CONTRAST (TbiRisk)=Indicator
  /CONTRAST (TbiCost)=Indicator
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.11	

[DataSet1] K:\[Emergency Medicine] Diagnostic Testing Research\Summer 2016 [Rahul & Chelsea]\Data\Rahul2016DiagnosticAccuracyUMHS.sav

Case Processing Summary	
Unweighted Casesa	N	Percent	
Selected Cases	Included in Analysis	913	89.8	
	Missing Cases	104	10.2	
	Total	1017	100.0	
Unselected Cases	0	.0	
Total	1017	100.0	

a. If weight is in effect, see classification table for the total number of cases.	

Dependent Variable Encoding	
Original Value	Internal Value	
No	0	
Yes	1	

Categorical Variables Codings	
	Frequency	Parameter coding	
		(1)	
TbiCost	0	458	1.000	
	100	455	.000	
TbiRisk	0.1	457	1.000	
	1	456	.000	
TbiBenefit	0.1	456	1.000	
	1	457	.000	

Block 0: Beginning Block

Classification Tablea,b	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 0	Would you agree to have this diagnostic test performed given the above information.	No	0	418	.0	
		Yes	0	495	100.0	
	Overall Percentage			54.2	

a. Constant is included in the model.	
b. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	
Step 0	Constant	.169	.066	6.479	1	.011	1.184	

Variables not in the Equation	
	Score	df	Sig.	
Step 0	Variables	TbiBenefit(1)	7.954	1	.005	
		TbiRisk(1)	9.524	1	.002	
		TbiCost(1)	12.570	1	.000	
	Overall Statistics	29.162	3	.000	

Block 1: Method = Enter

Omnibus Tests of Model Coefficients	
	Chi-square	df	Sig.	
Step 1	Step	29.527	3	.000	
	Block	29.527	3	.000	
	Model	29.527	3	.000	

Model Summary	
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square	
1	1229.658a	.032	.043	

a. Estimation terminated at iteration number 3 because parameter estimates changed by less than .001.	

Hosmer and Lemeshow Test	
Step	Chi-square	df	Sig.	
1	2.832	6	.830	

Contingency Table for Hosmer and Lemeshow Test	
	Would you agree to have this diagnostic test performed given the above information. = No	Would you agree to have this diagnostic test performed given the above information. = Yes	Total	
	Observed	Expected	Observed	Expected		
Step 1	1	66	71.527	51	45.473	117	
	2	67	62.020	53	57.980	120	
	3	63	59.628	54	57.372	117	
	4	53	51.499	50	51.501	103	
	5	39	41.824	62	59.176	101	
	6	46	46.953	70	69.047	116	
	7	48	47.345	71	71.655	119	
	8	36	37.203	84	82.797	120	

Classification Tablea	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 1	Would you agree to have this diagnostic test performed given the above information.	No	196	222	46.9	
		Yes	158	337	68.1	
	Overall Percentage			58.4	

a. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I.for EXP(B)	
							Lower	
Step 1a	TbiBenefit(1)	-.386	.135	8.129	1	.004	.680	.522	
	TbiRisk(1)	.414	.135	9.383	1	.002	1.513	1.161	
	TbiCost(1)	.453	.135	11.246	1	.001	1.573	1.207	
	Constant	-.067	.132	.259	1	.611	.935		

Variables in the Equation	
	95% C.I.for EXP(B)	
	Upper	
Step 1a	TbiBenefit(1)	.886	
	TbiRisk(1)	1.973	
	TbiCost(1)	2.050	
	Constant		

a. Variable(s) entered on step 1: TbiBenefit, TbiRisk, TbiCost.	

SORT CASES BY OutcomeTBI(A).

SAVE OUTFILE='K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 '+
    '[Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav'
  /COMPRESSED.
RECODE BenefitTBI1 (1=1) (MISSING=0) INTO BenefitTBIbinary.
VARIABLE LABELS  BenefitTBIbinary 'Indicator varibale for benefit'.
EXECUTE.
RECODE RiskTBI1 (1=1) (MISSING=0) INTO riskTBIbinary.
VARIABLE LABELS  riskTBIbinary 'High risk versus low risk'.
EXECUTE.
RECODE CostTBI100 (1=1) (MISSING=0) INTO CostTBIbinary.
VARIABLE LABELS  CostTBIbinary 'Incentive versus not'.
EXECUTE.
REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary.

Regression

Notes	
Output Created	23-APR-2019 16:55:40	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on cases with no missing values for any variable used.	
Syntax	REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary.	
Resources	Processor Time	00:00:00.06	
	Elapsed Time	00:00:00.07	
	Memory Required	25424 bytes	
	Additional Memory Required for Residual Plots	0 bytes	

[DataSet1] K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav

Variables Entered/Removeda	
Model	Variables Entered	Variables Removed	Method	
1	Incentive versus not, Indicator varibale for benefit, High risk versus low riskb	.	Enter	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. All requested variables entered.	

Model Summary	
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	
1	.179a	.032	.029	.491	

a. Predictors: (Constant), Incentive versus not, Indicator varibale for benefit, High risk versus low risk	

ANOVAa	
Model	Sum of Squares	df	Mean Square	F	Sig.	
1	Regression	7.239	3	2.413	9.997	.000b	
	Residual	219.388	909	.241			
	Total	226.627	912				

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors: (Constant), Incentive versus not, Indicator varibale for benefit, High risk versus low risk	

Coefficientsa	
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	
	B	Std. Error	Beta			
1	(Constant)	.600	.032		18.685	.000	
	Indicator varibale for benefit	.093	.033	.093	2.862	.004	
	High risk versus low risk	-.100	.033	-.101	-3.078	.002	
	Incentive versus not	-.110	.033	-.110	-3.374	.001	

Coefficientsa	
Model	Collinearity Statistics	
	Tolerance	VIF	
1	(Constant)			
	Indicator varibale for benefit	.998	1.002	
	High risk versus low risk	.997	1.003	
	Incentive versus not	.997	1.003	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

Collinearity Diagnosticsa	
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions	
				(Constant)	Indicator varibale for benefit	High risk versus low risk	
1	1	2.832	1.000	.03	.04	.04	
	2	.515	2.344	.00	.50	.00	
	3	.470	2.455	.00	.14	.72	
	4	.182	3.945	.97	.31	.24	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Incentive versus not	
1	1	.04	
	2	.46	
	3	.20	
	4	.30	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

SPSSINC CREATE DUMMIES VARIABLE=Q24 Q31 Q49.0 Numeracybinned AgeBinned
ROOTNAME1=ind
/OPTIONS ORDER=A USEVALUELABELS=YES USEML=YES OMITFIRST=NO.

Messages

Notes	
Output Created	23-APR-2019 17:01:20	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Syntax	BEGIN PROGRAM PYTHON2.	
Resources	Processor Time	00:00:00.00	
	Elapsed Time	00:00:00.01	

Warnings	
The number of root names does not match the number of variables: [u'ind']	

SPSSINC CREATE DUMMIES VARIABLE=Q24 Q31 Q49.0 Numeracybinned AgeBinned
ROOTNAME1=educ, health, income, numeracy, age
/OPTIONS ORDER=A USEVALUELABELS=YES USEML=YES OMITFIRST=NO.

Create dummy variables

Notes	
Output Created	23-APR-2019 17:02:13	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
Syntax	BEGIN PROGRAM '#
	'.	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.01	

Variable Creation	
	Label	
educ_1	Q24	
health_2	Q31	
income_3	Q49.0	
numeracy_4	Numeracybinned=Low numeracy	
numeracy_5	Numeracybinned=Medium numeracy	
numeracy_6	Numeracybinned=High numeracy	
age_7	AgeBinned=<= 25.00	
age_8	AgeBinned=26.00 - 40.00	
age_9	AgeBinned=41.00 - 55.00	
age_10	AgeBinned=56.00 - 65.00	
age_11	AgeBinned=66.00 - 75.00	
age_12	AgeBinned=76.00+	

SPSSINC CREATE DUMMIES VARIABLE=Q24 Q31 Q49.0 Numeracybinned AgeBinned
ROOTNAME1=educ, health, income, numeracy, age
/OPTIONS ORDER=A USEVALUELABELS=NO USEML=NO OMITFIRST=YES.

Create dummy variables

Notes	
Output Created	23-APR-2019 17:04:18	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
Syntax	BEGIN PROGRAM '#
	'.	
Resources	Processor Time	00:00:00.03	
	Elapsed Time	00:00:00.03	

Variable Creation	
	Label	
educ_1	Q24=-99.0	
educ_2	Q24=1.0	
educ_3	Q24=2.0	
educ_4	Q24=3.0	
educ_5	Q24=4.0	
educ_6	Q24=5.0	
health_7	Q31=-99.0	
health_8	Q31=1.0	
health_9	Q31=2.0	
health_10	Q31=3.0	
health_11	Q31=4.0	
health_12	Q31=5.0	
income_13	Q49.0=1.0	
income_14	Q49.0=2.0	
income_15	Q49.0=3.0	
income_16	Q49.0=4.0	
income_17	Q49.0=5.0	
income_18	Q49.0=6.0	
income_19	Q49.0=7.0	
income_20	Q49.0=8.0	
income_21	Q49.0=9.0	
income_22	Q49.0=10.0	
income_23	Q49.0=11.0	
numeracy_24	Numeracybinned=1.0	
numeracy_25	Numeracybinned=2.0	
numeracy_26	Numeracybinned=3.0	
age_27	AgeBinned=1.0	
age_28	AgeBinned=2.0	
age_29	AgeBinned=3.0	
age_30	AgeBinned=4.0	
age_31	AgeBinned=5.0	
age_32	AgeBinned=6.0	

SPSSINC CREATE DUMMIES VARIABLE=Q24 Q31 Q49.0 Numeracybinned AgeBinned
ROOTNAME1=educ, health, income, numeracy, age
/OPTIONS ORDER=A USEVALUELABELS=NO USEML=NO OMITFIRST=YES.

Create dummy variables

Notes	
Output Created	23-APR-2019 17:05:53	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
Syntax	BEGIN PROGRAM '#
	'.	
Resources	Processor Time	00:00:00.00	
	Elapsed Time	00:00:00.03	

Variable Creation	
	Label	
educ_1	Q24=-99.0	
educ_2	Q24=1.0	
educ_3	Q24=2.0	
educ_4	Q24=3.0	
educ_5	Q24=4.0	
educ_6	Q24=5.0	
health_7	Q31=-99.0	
health_8	Q31=1.0	
health_9	Q31=2.0	
health_10	Q31=3.0	
health_11	Q31=4.0	
health_12	Q31=5.0	
income_13	Q49.0=1.0	
income_14	Q49.0=2.0	
income_15	Q49.0=3.0	
income_16	Q49.0=4.0	
income_17	Q49.0=5.0	
income_18	Q49.0=6.0	
income_19	Q49.0=7.0	
income_20	Q49.0=8.0	
income_21	Q49.0=9.0	
income_22	Q49.0=10.0	
income_23	Q49.0=11.0	
numeracy_24	Numeracybinned=1.0	
numeracy_25	Numeracybinned=2.0	
numeracy_26	Numeracybinned=3.0	
age_27	AgeBinned=1.0	
age_28	AgeBinned=2.0	
age_29	AgeBinned=3.0	
age_30	AgeBinned=4.0	
age_31	AgeBinned=5.0	
age_32	AgeBinned=6.0	

LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q31 Q49.0
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q31)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary health_8 health_9 health_10 health_11
    health_12 income_13 income_14 income_15 income_16 income_17 income_18 income_19 income_20 income_21
    income_22 income_23 numeracy_24 numeracy_25 numeracy_26.

LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

Logistic Regression

Notes	
Output Created	23-APR-2019 17:20:04	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing	
Syntax	LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).	
Resources	Processor Time	00:00:00.03	
	Elapsed Time	00:00:00.04	

Case Processing Summary	
Unweighted Casesa	N	Percent	
Selected Cases	Included in Analysis	840	92.0	
	Missing Cases	73	8.0	
	Total	913	100.0	
Unselected Cases	0	.0	
Total	913	100.0	

a. If weight is in effect, see classification table for the total number of cases.	

Dependent Variable Encoding	
Original Value	Internal Value	
No	0	
Yes	1	

Categorical Variables Codings	
	Frequency	Parameter coding	
		(1)	(2)	(3)	
What is your household income level?	Less than $10,000	47	.000	.000	.000	
	$10,000 to $14,999	26	1.000	.000	.000	
	$15,000 to $24,999	33	.000	1.000	.000	
	$25,000 to $34,999	67	.000	.000	1.000	
	$35,000 to $49,999	54	.000	.000	.000	
	$50,000 to $74,999	89	.000	.000	.000	
	$75,000 to $99,999	68	.000	.000	.000	
	$100,000 to $149,999	89	.000	.000	.000	
	$150,000 to $199,999	29	.000	.000	.000	
	$200,000 or more	48	.000	.000	.000	
	Prefer not to respond	290	.000	.000	.000	
What level of education have you achieved?	Some high school	36	.000	.000	.000	
	High school graduate	139	1.000	.000	.000	
	Some college	285	.000	1.000	.000	
	College graduate	236	.000	.000	1.000	
	Post graduate degree (Master's, PhD, MD, JD, MSW)	144	.000	.000	.000	
Numeracyscore (Binned)	Low numeracy	99	.000	.000		
	Medium numeracy	247	1.000	.000		
	High numeracy	494	.000	1.000		
TbiRisk	0.1	421	.000			
	1	419	1.000			
TbiCost	0	420	.000			
	100	420	1.000			
TbiBenefit	0.1	416	.000			
	1	424	1.000			

Categorical Variables Codings	
	Parameter coding	
	(4)	(5)	(6)	(7)	
What is your household income level?	Less than $10,000	.000	.000	.000	.000	
	$10,000 to $14,999	.000	.000	.000	.000	
	$15,000 to $24,999	.000	.000	.000	.000	
	$25,000 to $34,999	.000	.000	.000	.000	
	$35,000 to $49,999	1.000	.000	.000	.000	
	$50,000 to $74,999	.000	1.000	.000	.000	
	$75,000 to $99,999	.000	.000	1.000	.000	
	$100,000 to $149,999	.000	.000	.000	1.000	
	$150,000 to $199,999	.000	.000	.000	.000	
	$200,000 or more	.000	.000	.000	.000	
	Prefer not to respond	.000	.000	.000	.000	
What level of education have you achieved?	Some high school	.000				
	High school graduate	.000				
	Some college	.000				
	College graduate	.000				
	Post graduate degree (Master's, PhD, MD, JD, MSW)	1.000				
Numeracyscore (Binned)	Low numeracy					
	Medium numeracy					
	High numeracy					
TbiRisk	0.1					
	1					
TbiCost	0					
	100					
TbiBenefit	0.1					
	1					

Categorical Variables Codings	
	Parameter coding	
	(8)	(9)	(10)	
What is your household income level?	Less than $10,000	.000	.000	.000	
	$10,000 to $14,999	.000	.000	.000	
	$15,000 to $24,999	.000	.000	.000	
	$25,000 to $34,999	.000	.000	.000	
	$35,000 to $49,999	.000	.000	.000	
	$50,000 to $74,999	.000	.000	.000	
	$75,000 to $99,999	.000	.000	.000	
	$100,000 to $149,999	.000	.000	.000	
	$150,000 to $199,999	1.000	.000	.000	
	$200,000 or more	.000	1.000	.000	
	Prefer not to respond	.000	.000	1.000	
What level of education have you achieved?	Some high school				
	High school graduate				
	Some college				
	College graduate				
	Post graduate degree (Master's, PhD, MD, JD, MSW)				
Numeracyscore (Binned)	Low numeracy				
	Medium numeracy				
	High numeracy				
TbiRisk	0.1				
	1				
TbiCost	0				
	100				
TbiBenefit	0.1				
	1				

Block 0: Beginning Block

Classification Tablea,b	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 0	Would you agree to have this diagnostic test performed given the above information.	No	0	380	.0	
		Yes	0	460	100.0	
	Overall Percentage			54.8	

a. Constant is included in the model.	
b. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	
Step 0	Constant	.191	.069	7.596	1	.006	1.211	

Variables not in the Equation	
	Score	df	Sig.	
Step 0	Variables	TbiBenefit(1)	6.097	1	.014	
		TbiRisk(1)	7.274	1	.007	
		TbiCost(1)	12.994	1	.000	
		Numeracyscore (Binned)	8.308	2	.016	
		Numeracyscore (Binned)(1)	8.128	1	.004	
		Numeracyscore (Binned)(2)	6.092	1	.014	
		What is your household income level?	12.059	10	.281	
		What is your household income level?(1)	5.319	1	.021	
		What is your household income level?(2)	1.965	1	.161	
		What is your household income level?(3)	.349	1	.555	
		What is your household income level?(4)	.471	1	.492	
		What is your household income level?(5)	.081	1	.776	
		What is your household income level?(6)	.004	1	.952	
		What is your household income level?(7)	1.139	1	.286	
		What is your household income level?(8)	2.171	1	.141	
		What is your household income level?(9)	.963	1	.326	
		What is your household income level?(10)	.168	1	.682	
		What level of education have you achieved?	6.531	4	.163	
		What level of education have you achieved?(1)	4.120	1	.042	
		What level of education have you achieved?(2)	.080	1	.778	
		What level of education have you achieved?(3)	2.493	1	.114	
		What level of education have you achieved?(4)	.798	1	.372	
	Overall Statistics	44.007	19	.001	

Block 1: Method = Enter

Omnibus Tests of Model Coefficients	
	Chi-square	df	Sig.	
Step 1	Step	45.438	19	.001	
	Block	45.438	19	.001	
	Model	45.438	19	.001	

Model Summary	
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square	
1	1111.418a	.053	.070	

a. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.	

Hosmer and Lemeshow Test	
Step	Chi-square	df	Sig.	
1	6.815	8	.557	

Contingency Table for Hosmer and Lemeshow Test	
	Would you agree to have this diagnostic test performed given the above information. = No	Would you agree to have this diagnostic test performed given the above information. = Yes	Total	
	Observed	Expected	Observed	Expected		
Step 1	1	56	54.808	29	30.192	85	
	2	41	48.896	44	36.104	85	
	3	44	44.823	40	39.177	84	
	4	43	42.091	42	42.909	85	
	5	42	39.071	42	44.929	84	
	6	42	36.023	40	45.977	82	
	7	39	35.844	48	51.156	87	
	8	31	32.130	55	53.870	86	
	9	23	27.171	60	55.829	83	
	10	19	19.143	60	59.857	79	

Classification Tablea	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 1	Would you agree to have this diagnostic test performed given the above information.	No	161	219	42.4	
		Yes	125	335	72.8	
	Overall Percentage			59.0	

a. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I.for EXP(B)	
							Lower	Upper	
Step 1a	TbiBenefit(1)	.380	.144	6.931	1	.008	1.462	1.102	1.940	
	TbiRisk(1)	-.360	.144	6.230	1	.013	.698	.526	.926	
	TbiCost(1)	-.486	.144	11.484	1	.001	.615	.464	.815	
	Numeracyscore (Binned)			3.956	2	.138				
	Numeracyscore (Binned)(1)	.259	.249	1.086	1	.297	1.296	.796	2.112	
	Numeracyscore (Binned)(2)	-.089	.234	.144	1	.704	.915	.579	1.447	
	What is your household income level?			6.997	10	.726				
	What is your household income level?(1)	.810	.561	2.082	1	.149	2.247	.748	6.751	
	What is your household income level?(2)	.526	.484	1.182	1	.277	1.692	.656	4.368	
	What is your household income level?(3)	.240	.401	.358	1	.550	1.271	.579	2.790	
	What is your household income level?(4)	.191	.419	.209	1	.648	1.211	.533	2.754	
	What is your household income level?(5)	.198	.381	.270	1	.603	1.219	.578	2.569	
	What is your household income level?(6)	.190	.402	.223	1	.637	1.209	.550	2.657	
	What is your household income level?(7)	-.022	.389	.003	1	.955	.978	.456	2.097	
	What is your household income level?(8)	-.378	.506	.559	1	.455	.685	.254	1.846	
	What is your household income level?(9)	-.139	.438	.101	1	.750	.870	.369	2.052	
	What is your household income level?(10)	.022	.327	.005	1	.946	1.022	.538	1.942	
	What level of education have you achieved?			2.076	4	.722				
	What level of education have you achieved?(1)	.111	.401	.076	1	.782	1.117	.509	2.451	
	What level of education have you achieved?(2)	-.145	.387	.140	1	.708	.865	.405	1.847	
	What level of education have you achieved?(3)	-.216	.404	.286	1	.593	.806	.365	1.777	
	What level of education have you achieved?(4)	-.203	.422	.232	1	.630	.816	.357	1.865	
	Constant	.440	.470	.874	1	.350	1.552			

a. Variable(s) entered on step 1: TbiBenefit, TbiRisk, TbiCost, Numeracyscore (Binned), What is your household income level?, What level of education have you achieved?.	

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary numeracy_24 numeracy_25 numeracy_26
    educ_2 educ_3 educ_4 educ_5 educ_6.

Regression

Notes	
Output Created	23-APR-2019 17:20:52	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on cases with no missing values for any variable used.	
Syntax	REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary numeracy_24 numeracy_25 numeracy_26
    educ_2 educ_3 educ_4 educ_5 educ_6.	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.04	
	Memory Required	33168 bytes	
	Additional Memory Required for Residual Plots	0 bytes	

Variables Entered/Removeda	
Model	Variables Entered	Variables Removed	Method	
1	Q24=5.0, High risk versus low risk, Incentive versus not, Numeracybinned=1.0, Indicator varibale for benefit, Q24=1.0, Q24=2.0, Numeracybinned=2.0, Q24=4.0b	.	Enter	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Tolerance = .000 limit reached.	

Model Summary	
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	
1	.209a	.044	.033	.490	

a. Predictors: (Constant), Q24=5.0, High risk versus low risk, Incentive versus not, Numeracybinned=1.0, Indicator varibale for benefit, Q24=1.0, Q24=2.0, Numeracybinned=2.0, Q24=4.0	

ANOVAa	
Model	Sum of Squares	df	Mean Square	F	Sig.	
1	Regression	9.193	9	1.021	4.259	.000b	
	Residual	201.220	839	.240			
	Total	210.412	848				

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors: (Constant), Q24=5.0, High risk versus low risk, Incentive versus not, Numeracybinned=1.0, Indicator varibale for benefit, Q24=1.0, Q24=2.0, Numeracybinned=2.0, Q24=4.0	

Coefficientsa	
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	
	B	Std. Error	Beta			
1	(Constant)	.583	.044		13.128	.000	
	Indicator varibale for benefit	.085	.034	.085	2.516	.012	
	High risk versus low risk	-.094	.034	-.094	-2.789	.005	
	Incentive versus not	-.113	.034	-.113	-3.339	.001	
	Numeracybinned=1.0	.003	.053	.002	.061	.951	
	Numeracybinned=2.0	.089	.041	.081	2.193	.029	
	Q24=1.0	.043	.088	.017	.492	.623	
	Q24=2.0	.058	.051	.043	1.134	.257	
	Q24=4.0	-.033	.044	-.030	-.760	.448	
	Q24=5.0	-.030	.051	-.022	-.586	.558	

Coefficientsa	
Model	Collinearity Statistics	
	Tolerance	VIF	
1	(Constant)			
	Indicator varibale for benefit	.989	1.011	
	High risk versus low risk	.994	1.006	
	Incentive versus not	.990	1.010	
	Numeracybinned=1.0	.906	1.104	
	Numeracybinned=2.0	.830	1.205	
	Q24=1.0	.905	1.105	
	Q24=2.0	.789	1.268	
	Q24=4.0	.732	1.367	
	Q24=5.0	.780	1.283	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

Excluded Variablesa	
Model	Beta In	t	Sig.	Partial Correlation	Collinearity Statistics	
					Tolerance	
1	Numeracybinned=3.0	.b	.	.	.	.000	
	Q24=3.0	.b	.	.	.	.000	

Excluded Variablesa	
Model	Collinearity Statistics	
	VIF	Minimum Tolerance	
1	Numeracybinned=3.0	.	.000	
	Q24=3.0	.	.000	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors in the Model: (Constant), Q24=5.0, High risk versus low risk, Incentive versus not, Numeracybinned=1.0, Indicator varibale for benefit, Q24=1.0, Q24=2.0, Numeracybinned=2.0, Q24=4.0	

Collinearity Diagnosticsa	
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions	
				(Constant)	Indicator varibale for benefit	High risk versus low risk	
1	1	3.917	1.000	.01	.02	.02	
	2	1.145	1.849	.00	.01	.00	
	3	1.037	1.944	.00	.00	.00	
	4	1.003	1.976	.00	.00	.00	
	5	.937	2.044	.00	.00	.00	
	6	.523	2.738	.00	.34	.00	
	7	.482	2.850	.00	.28	.39	
	8	.464	2.906	.00	.02	.47	
	9	.384	3.194	.01	.17	.00	
	10	.109	6.007	.98	.15	.11	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Incentive versus not	Numeracybinned=1.0	Numeracybinned=2.0	Q24=1.0	Q24=2.0	
1	1	.02	.01	.01	.00	.01	
	2	.00	.02	.13	.08	.18	
	3	.00	.20	.01	.40	.03	
	4	.00	.00	.00	.15	.04	
	5	.00	.41	.05	.09	.15	
	6	.51	.03	.05	.01	.02	
	7	.03	.08	.18	.07	.11	
	8	.01	.17	.40	.06	.04	
	9	.31	.01	.02	.10	.28	
	10	.12	.08	.14	.04	.14	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Q24=4.0	Q24=5.0	
1	1	.01	.01	
	2	.08	.05	
	3	.02	.10	
	4	.14	.31	
	5	.06	.00	
	6	.00	.02	
	7	.00	.00	
	8	.03	.04	
	9	.34	.24	
	10	.32	.23	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24 AgeBinned Q17 Q30
    whitevsnonwhite
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /CONTRAST (whitevsnonwhite)=Indicator(1)
  /CONTRAST (Q30)=Indicator(1)
  /CONTRAST (Q17)=Indicator(1)
  /CONTRAST (AgeBinned)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

RECODE Q30 (1=0) (ELSE=1) INTO medprof.
VARIABLE LABELS  medprof 'med indicator'.
EXECUTE.
DESCRIPTIVES VARIABLES=Q17
  /STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Notes	
Output Created	23-APR-2019 17:28:14	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User defined missing values are treated as missing.	
	Cases Used	All non-missing data are used.	
Syntax	DESCRIPTIVES VARIABLES=Q17
  /STATISTICS=MEAN STDDEV MIN MAX.	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.02	

Descriptive Statistics	
	N	Minimum	Maximum	Mean	Std. Deviation	
What is your gender	907	-99	3	-1.96	18.574	
Valid N (listwise)	907					

FREQUENCIES VARIABLES=Q17
  /ORDER=ANALYSIS.

Frequencies

Notes	
Output Created	23-APR-2019 17:28:49	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on all cases with valid data.	
Syntax	FREQUENCIES VARIABLES=Q17
  /ORDER=ANALYSIS.	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.01	

Statistics	
What is your gender  	
N	Valid	907	
	Missing	6	

What is your gender	
	Frequency	Percent	Valid Percent	Cumulative Percent	
Valid	-99	32	3.5	3.5	3.5	
	Male	362	39.6	39.9	43.4	
	2	512	56.1	56.4	99.9	
	3	1	.1	.1	100.0	
	Total	907	99.3	100.0		
Missing	System	6	.7			
Total	913	100.0			

RECODE Q17 (MISSING=SYSMIS) (2=0) (ELSE=1) INTO male.
VARIABLE LABELS  male 'male'.
EXECUTE.
FREQUENCIES VARIABLES=male
  /ORDER=ANALYSIS.

Frequencies

Notes	
Output Created	23-APR-2019 17:30:48	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on all cases with valid data.	
Syntax	FREQUENCIES VARIABLES=male
  /ORDER=ANALYSIS.	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.02	

Statistics	
male  	
N	Valid	907	
	Missing	6	

male	
	Frequency	Percent	Valid Percent	Cumulative Percent	
Valid	.00	512	56.1	56.4	56.4	
	1.00	395	43.3	43.6	100.0	
	Total	907	99.3	100.0		
Missing	System	6	.7			
Total	913	100.0			

RECODE Q17 (MISSING=SYSMIS) (2=0) (-99=SYSMIS) (ELSE=1) INTO male.
VARIABLE LABELS  male 'male'.
EXECUTE.
FREQUENCIES VARIABLES=male
  /ORDER=ANALYSIS.

Frequencies

Notes	
Output Created	23-APR-2019 17:31:42	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on all cases with valid data.	
Syntax	FREQUENCIES VARIABLES=male
  /ORDER=ANALYSIS.	
Resources	Processor Time	00:00:00.02	
	Elapsed Time	00:00:00.02	

Statistics	
male  	
N	Valid	875	
	Missing	38	

male	
	Frequency	Percent	Valid Percent	Cumulative Percent	
Valid	.00	512	56.1	58.5	58.5	
	1.00	363	39.8	41.5	100.0	
	Total	875	95.8	100.0		
Missing	System	38	4.2			
Total	913	100.0			

LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24 AgeBinned whitevsnonwhite
    medprof male
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /CONTRAST (whitevsnonwhite)=Indicator(1)
  /CONTRAST (AgeBinned)=Indicator(1)
  /CONTRAST (medprof)=Indicator(1)
  /CONTRAST (male)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

Logistic Regression

Notes	
Output Created	23-APR-2019 17:33:48	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing	
Syntax	LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24 AgeBinned whitevsnonwhite
    medprof male
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /CONTRAST (whitevsnonwhite)=Indicator(1)
  /CONTRAST (AgeBinned)=Indicator(1)
  /CONTRAST (medprof)=Indicator(1)
  /CONTRAST (male)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).	
Resources	Processor Time	00:00:00.05	
	Elapsed Time	00:00:00.06	

Case Processing Summary	
Unweighted Casesa	N	Percent	
Selected Cases	Included in Analysis	838	91.8	
	Missing Cases	75	8.2	
	Total	913	100.0	
Unselected Cases	0	.0	
Total	913	100.0	

a. If weight is in effect, see classification table for the total number of cases.	

Dependent Variable Encoding	
Original Value	Internal Value	
No	0	
Yes	1	

Categorical Variables Codings	
	Frequency	Parameter coding	
		(1)	(2)	(3)	(4)	(5)	
What is your household income level?	Less than $10,000	47	.000	.000	.000	.000	.000	
	$10,000 to $14,999	26	1.000	.000	.000	.000	.000	
	$15,000 to $24,999	33	.000	1.000	.000	.000	.000	
	$25,000 to $34,999	66	.000	.000	1.000	.000	.000	
	$35,000 to $49,999	54	.000	.000	.000	1.000	.000	
	$50,000 to $74,999	89	.000	.000	.000	.000	1.000	
	$75,000 to $99,999	68	.000	.000	.000	.000	.000	
	$100,000 to $149,999	89	.000	.000	.000	.000	.000	
	$150,000 to $199,999	29	.000	.000	.000	.000	.000	
	$200,000 or more	48	.000	.000	.000	.000	.000	
	Prefer not to respond	289	.000	.000	.000	.000	.000	
What is your age? (Binned)	<= 25.00	138	.000	.000	.000	.000	.000	
	26.00 - 40.00	203	1.000	.000	.000	.000	.000	
	41.00 - 55.00	226	.000	1.000	.000	.000	.000	
	56.00 - 65.00	132	.000	.000	1.000	.000	.000	
	66.00 - 75.00	93	.000	.000	.000	1.000	.000	
	76.00+	46	.000	.000	.000	.000	1.000	
What level of education have you achieved?	Some high school	36	.000	.000	.000	.000		
	High school graduate	139	1.000	.000	.000	.000		
	Some college	285	.000	1.000	.000	.000		
	College graduate	235	.000	.000	1.000	.000		
	Post graduate degree (Master's, PhD, MD, JD, MSW)	143	.000	.000	.000	1.000		
Numeracyscore (Binned)	Low numeracy	99	.000	.000				
	Medium numeracy	247	1.000	.000				
	High numeracy	492	.000	1.000				
TbiRisk	0.1	421	.000					
	1	417	1.000					
TbiCost	0	420	.000					
	100	418	1.000					
male	.00	489	.000					
	1.00	349	1.000					
med indicator	.00	616	.000					
	1.00	222	1.000					
whitevsnonwhite	0	153	.000					
	white	685	1.000					
TbiBenefit	0.1	415	.000					
	1	423	1.000					

Categorical Variables Codings	
	Parameter coding	
	(6)	(7)	(8)	(9)	(10)	
What is your household income level?	Less than $10,000	.000	.000	.000	.000	.000	
	$10,000 to $14,999	.000	.000	.000	.000	.000	
	$15,000 to $24,999	.000	.000	.000	.000	.000	
	$25,000 to $34,999	.000	.000	.000	.000	.000	
	$35,000 to $49,999	.000	.000	.000	.000	.000	
	$50,000 to $74,999	.000	.000	.000	.000	.000	
	$75,000 to $99,999	1.000	.000	.000	.000	.000	
	$100,000 to $149,999	.000	1.000	.000	.000	.000	
	$150,000 to $199,999	.000	.000	1.000	.000	.000	
	$200,000 or more	.000	.000	.000	1.000	.000	
	Prefer not to respond	.000	.000	.000	.000	1.000	
What is your age? (Binned)	<= 25.00						
	26.00 - 40.00						
	41.00 - 55.00						
	56.00 - 65.00						
	66.00 - 75.00						
	76.00+						
What level of education have you achieved?	Some high school						
	High school graduate						
	Some college						
	College graduate						
	Post graduate degree (Master's, PhD, MD, JD, MSW)						
Numeracyscore (Binned)	Low numeracy						
	Medium numeracy						
	High numeracy						
TbiRisk	0.1						
	1						
TbiCost	0						
	100						
male	.00						
	1.00						
med indicator	.00						
	1.00						
whitevsnonwhite	0						
	white						
TbiBenefit	0.1						
	1						

Block 0: Beginning Block

Classification Tablea,b	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 0	Would you agree to have this diagnostic test performed given the above information.	No	0	380	.0	
		Yes	0	458	100.0	
	Overall Percentage			54.7	

a. Constant is included in the model.	
b. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	
Step 0	Constant	.187	.069	7.239	1	.007	1.205	

Variables not in the Equation	
	Score	df	Sig.	
Step 0	Variables	TbiBenefit(1)	6.112	1	.013	
		TbiRisk(1)	7.633	1	.006	
		TbiCost(1)	13.478	1	.000	
		Numeracyscore (Binned)	8.577	2	.014	
		Numeracyscore (Binned)(1)	8.366	1	.004	
		Numeracyscore (Binned)(2)	6.362	1	.012	
		What is your household income level?	11.979	10	.286	
		What is your household income level?(1)	5.369	1	.020	
		What is your household income level?(2)	2.000	1	.157	
		What is your household income level?(3)	.247	1	.619	
		What is your household income level?(4)	.494	1	.482	
		What is your household income level?(5)	.094	1	.760	
		What is your household income level?(6)	.002	1	.967	
		What is your household income level?(7)	1.093	1	.296	
		What is your household income level?(8)	2.136	1	.144	
		What is your household income level?(9)	.933	1	.334	
		What is your household income level?(10)	.185	1	.667	
		What level of education have you achieved?	6.829	4	.145	
		What level of education have you achieved?(1)	4.235	1	.040	
		What level of education have you achieved?(2)	.107	1	.743	
		What level of education have you achieved?(3)	2.599	1	.107	
		What level of education have you achieved?(4)	.904	1	.342	
		What is your age? (Binned)	10.799	5	.056	
		What is your age? (Binned)(1)	1.657	1	.198	
		What is your age? (Binned)(2)	2.686	1	.101	
		What is your age? (Binned)(3)	4.505	1	.034	
		What is your age? (Binned)(4)	3.259	1	.071	
		What is your age? (Binned)(5)	.759	1	.384	
		whitevsnonwhite(1)	6.671	1	.010	
		med indicator(1)	1.329	1	.249	
		male(1)	.011	1	.917	
	Overall Statistics	62.592	27	.000	

Block 1: Method = Enter

Omnibus Tests of Model Coefficients	
	Chi-square	df	Sig.	
Step 1	Step	65.013	27	.000	
	Block	65.013	27	.000	
	Model	65.013	27	.000	

Model Summary	
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square	
1	1089.431a	.075	.100	

a. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.	

Hosmer and Lemeshow Test	
Step	Chi-square	df	Sig.	
1	3.803	8	.874	

Contingency Table for Hosmer and Lemeshow Test	
	Would you agree to have this diagnostic test performed given the above information. = No	Would you agree to have this diagnostic test performed given the above information. = Yes	Total	
	Observed	Expected	Observed	Expected		
Step 1	1	52	56.828	32	27.172	84	
	2	50	50.653	34	33.347	84	
	3	50	46.873	34	37.127	84	
	4	42	43.629	42	40.371	84	
	5	43	40.319	41	43.681	84	
	6	40	36.492	44	47.508	84	
	7	35	32.987	49	51.013	84	
	8	30	29.374	54	54.626	84	
	9	22	24.965	62	59.035	84	
	10	16	17.880	66	64.120	82	

Classification Tablea	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 1	Would you agree to have this diagnostic test performed given the above information.	No	196	184	51.6	
		Yes	144	314	68.6	
	Overall Percentage			60.9	

a. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I.for EXP(B)	
							Lower	Upper	
Step 1a	TbiBenefit(1)	.412	.147	7.792	1	.005	1.509	1.130	2.015	
	TbiRisk(1)	-.380	.147	6.694	1	.010	.684	.513	.912	
	TbiCost(1)	-.483	.146	11.019	1	.001	.617	.464	.820	
	Numeracyscore (Binned)			3.977	2	.137				
	Numeracyscore (Binned)(1)	.311	.257	1.469	1	.225	1.365	.825	2.258	
	Numeracyscore (Binned)(2)	-.041	.240	.030	1	.863	.959	.599	1.537	
	What is your household income level?			7.725	10	.656				
	What is your household income level?(1)	.761	.569	1.786	1	.181	2.139	.701	6.527	
	What is your household income level?(2)	.527	.491	1.151	1	.283	1.694	.647	4.437	
	What is your household income level?(3)	.181	.410	.195	1	.659	1.198	.537	2.676	
	What is your household income level?(4)	.264	.432	.374	1	.541	1.303	.558	3.040	
	What is your household income level?(5)	.137	.390	.124	1	.725	1.147	.534	2.463	
	What is your household income level?(6)	.141	.410	.118	1	.731	1.152	.515	2.573	
	What is your household income level?(7)	-.132	.399	.110	1	.741	.876	.400	1.917	
	What is your household income level?(8)	-.429	.515	.695	1	.404	.651	.237	1.785	
	What is your household income level?(9)	-.157	.449	.123	1	.726	.854	.354	2.060	
	What is your household income level?(10)	-.062	.335	.034	1	.854	.940	.487	1.814	
	What level of education have you achieved?			1.136	4	.888				
	What level of education have you achieved?(1)	.210	.408	.265	1	.607	1.234	.554	2.747	
	What level of education have you achieved?(2)	.029	.395	.005	1	.941	1.030	.475	2.231	
	What level of education have you achieved?(3)	-.039	.412	.009	1	.924	.962	.429	2.155	
	What level of education have you achieved?(4)	-.037	.433	.007	1	.933	.964	.412	2.254	
	What is your age? (Binned)			11.667	5	.040				
	What is your age? (Binned)(1)	.073	.242	.090	1	.764	1.076	.669	1.729	
	What is your age? (Binned)(2)	.489	.240	4.158	1	.041	1.630	1.019	2.608	
	What is your age? (Binned)(3)	-.035	.266	.017	1	.895	.966	.574	1.626	
	What is your age? (Binned)(4)	.637	.298	4.556	1	.033	1.891	1.053	3.393	
	What is your age? (Binned)(5)	.473	.376	1.586	1	.208	1.605	.769	3.350	
	whitevsnonwhite(1)	-.471	.199	5.603	1	.018	.624	.422	.922	
	med indicator(1)	-.171	.173	.972	1	.324	.843	.600	1.184	
	male(1)	-.023	.155	.022	1	.881	.977	.722	1.323	
	Constant	.490	.526	.869	1	.351	1.633			

a. Variable(s) entered on step 1: TbiBenefit, TbiRisk, TbiCost, Numeracyscore (Binned), What is your household income level?, What level of education have you achieved?, What is your age? (Binned), whitevsnonwhite, med indicator, male.	

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary numeracy_24 numeracy_25 numeracy_26
    educ_2 educ_3 educ_4 educ_5 educ_6 male medprof whitevsnonwhite.

Regression

Notes	
Output Created	23-APR-2019 17:34:55	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on cases with no missing values for any variable used.	
Syntax	REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary numeracy_24 numeracy_25 numeracy_26
    educ_2 educ_3 educ_4 educ_5 educ_6 male medprof whitevsnonwhite.	
Resources	Processor Time	00:00:00.03	
	Elapsed Time	00:00:00.05	
	Memory Required	36720 bytes	
	Additional Memory Required for Residual Plots	0 bytes	

Variables Entered/Removeda	
Model	Variables Entered	Variables Removed	Method	
1	whitevsnonwhite, High risk versus low risk, Numeracybinned=1.0, Q24=2.0, Incentive versus not, male, Indicator varibale for benefit, Q24=1.0, Q24=5.0, med indicator, Numeracybinned=2.0, Q24=4.0b	.	Enter	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Tolerance = .000 limit reached.	

Model Summary	
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	
1	.229a	.052	.039	.488	

a. Predictors: (Constant), whitevsnonwhite, High risk versus low risk, Numeracybinned=1.0, Q24=2.0, Incentive versus not, male, Indicator varibale for benefit, Q24=1.0, Q24=5.0, med indicator, Numeracybinned=2.0, Q24=4.0	

ANOVAa	
Model	Sum of Squares	df	Mean Square	F	Sig.	
1	Regression	10.952	12	.913	3.825	.000b	
	Residual	198.750	833	.239			
	Total	209.702	845				

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors: (Constant), whitevsnonwhite, High risk versus low risk, Numeracybinned=1.0, Q24=2.0, Incentive versus not, male, Indicator varibale for benefit, Q24=1.0, Q24=5.0, med indicator, Numeracybinned=2.0, Q24=4.0	

Coefficientsa	
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	
	B	Std. Error	Beta			
1	(Constant)	.674	.059		11.457	.000	
	Indicator varibale for benefit	.090	.034	.090	2.649	.008	
	High risk versus low risk	-.096	.034	-.096	-2.834	.005	
	Incentive versus not	-.118	.034	-.119	-3.498	.000	
	Numeracybinned=1.0	-.001	.053	-.001	-.027	.978	
	Numeracybinned=2.0	.086	.041	.078	2.088	.037	
	Q24=1.0	.025	.088	.010	.282	.778	
	Q24=2.0	.058	.051	.043	1.135	.257	
	Q24=4.0	-.020	.044	-.018	-.458	.647	
	Q24=5.0	-.017	.051	-.013	-.339	.735	
	male	.002	.036	.002	.047	.962	
	med indicator	-.034	.040	-.030	-.849	.396	
	whitevsnonwhite	-.104	.044	-.081	-2.346	.019	

Coefficientsa	
Model	Collinearity Statistics	
	Tolerance	VIF	
1	(Constant)			
	Indicator varibale for benefit	.981	1.019	
	High risk versus low risk	.993	1.007	
	Incentive versus not	.989	1.011	
	Numeracybinned=1.0	.900	1.111	
	Numeracybinned=2.0	.813	1.231	
	Q24=1.0	.891	1.123	
	Q24=2.0	.782	1.278	
	Q24=4.0	.722	1.385	
	Q24=5.0	.761	1.315	
	male	.917	1.091	
	med indicator	.900	1.111	
	whitevsnonwhite	.958	1.044	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

Excluded Variablesa	
Model	Beta In	t	Sig.	Partial Correlation	Collinearity Statistics	
					Tolerance	
1	Numeracybinned=3.0	.b	.	.	.	.000	
	Q24=3.0	.b	.	.	.	.000	

Excluded Variablesa	
Model	Collinearity Statistics	
	VIF	Minimum Tolerance	
1	Numeracybinned=3.0	.	.000	
	Q24=3.0	.	.000	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors in the Model: (Constant), whitevsnonwhite, High risk versus low risk, Numeracybinned=1.0, Q24=2.0, Incentive versus not, male, Indicator varibale for benefit, Q24=1.0, Q24=5.0, med indicator, Numeracybinned=2.0, Q24=4.0	

Collinearity Diagnosticsa	
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions	
				(Constant)	Indicator varibale for benefit	High risk versus low risk	
1	1	5.415	1.000	.00	.01	.01	
	2	1.198	2.126	.00	.00	.00	
	3	1.094	2.225	.00	.00	.00	
	4	1.011	2.315	.00	.00	.00	
	5	.972	2.360	.00	.00	.00	
	6	.726	2.731	.00	.02	.01	
	7	.529	3.199	.00	.25	.06	
	8	.511	3.256	.00	.13	.37	
	9	.453	3.456	.00	.23	.26	
	10	.444	3.494	.00	.14	.21	
	11	.367	3.841	.00	.08	.00	
	12	.217	4.999	.02	.09	.03	
	13	.064	9.217	.98	.04	.05	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Incentive versus not	Numeracybinned=1.0	Numeracybinned=2.0	Q24=1.0	Q24=2.0	
1	1	.01	.00	.01	.00	.00	
	2	.00	.02	.05	.13	.15	
	3	.00	.27	.12	.04	.05	
	4	.00	.01	.00	.30	.06	
	5	.00	.12	.04	.23	.10	
	6	.00	.29	.00	.00	.03	
	7	.32	.06	.12	.02	.06	
	8	.17	.02	.03	.02	.06	
	9	.30	.00	.00	.00	.00	
	10	.00	.14	.48	.11	.09	
	11	.12	.00	.01	.12	.24	
	12	.02	.03	.04	.01	.12	
	13	.06	.04	.10	.02	.04	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Q24=4.0	Q24=5.0	male	med indicator	whitevsnonwhite	
1	1	.01	.00	.01	.01	.00	
	2	.04	.09	.01	.07	.00	
	3	.04	.02	.03	.04	.00	
	4	.11	.20	.00	.01	.00	
	5	.09	.09	.01	.02	.00	
	6	.00	.03	.17	.36	.00	
	7	.00	.01	.07	.04	.00	
	8	.01	.00	.13	.11	.00	
	9	.00	.00	.21	.14	.00	
	10	.03	.04	.07	.02	.00	
	11	.32	.27	.20	.14	.02	
	12	.29	.20	.04	.02	.49	
	13	.07	.05	.05	.03	.48	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24 AgeBinned whitevsnonwhite
    medprof male Q19 Q20 Q21 Q22 Q23 Q56.0 Q31
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /CONTRAST (whitevsnonwhite)=Indicator(1)
  /CONTRAST (AgeBinned)=Indicator(1)
  /CONTRAST (medprof)=Indicator(1)
  /CONTRAST (male)=Indicator(1)
  /CONTRAST (Q19)=Indicator(1)
  /CONTRAST (Q20)=Indicator(1)
  /CONTRAST (Q21)=Indicator(1)
  /CONTRAST (Q22)=Indicator(1)
  /CONTRAST (Q23)=Indicator(1)
  /CONTRAST (Q56.0)=Indicator(1)
  /CONTRAST (Q31)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).

Logistic Regression

Notes	
Output Created	23-APR-2019 17:38:12	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing	
Syntax	LOGISTIC REGRESSION VARIABLES OutcomeTBI
  /METHOD=ENTER TbiBenefit TbiRisk TbiCost Numeracybinned Q49.0 Q24 AgeBinned whitevsnonwhite
    medprof male Q19 Q20 Q21 Q22 Q23 Q56.0 Q31
  /CONTRAST (TbiBenefit)=Indicator(1)
  /CONTRAST (TbiRisk)=Indicator(1)
  /CONTRAST (TbiCost)=Indicator(1)
  /CONTRAST (Numeracybinned)=Indicator(1)
  /CONTRAST (Q49.0)=Indicator(1)
  /CONTRAST (Q24)=Indicator(1)
  /CONTRAST (whitevsnonwhite)=Indicator(1)
  /CONTRAST (AgeBinned)=Indicator(1)
  /CONTRAST (medprof)=Indicator(1)
  /CONTRAST (male)=Indicator(1)
  /CONTRAST (Q19)=Indicator(1)
  /CONTRAST (Q20)=Indicator(1)
  /CONTRAST (Q21)=Indicator(1)
  /CONTRAST (Q22)=Indicator(1)
  /CONTRAST (Q23)=Indicator(1)
  /CONTRAST (Q56.0)=Indicator(1)
  /CONTRAST (Q31)=Indicator(1)
  /PRINT=GOODFIT CI(95)
  /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).	
Resources	Processor Time	00:00:00.09	
	Elapsed Time	00:00:00.08	

Case Processing Summary	
Unweighted Casesa	N	Percent	
Selected Cases	Included in Analysis	833	91.2	
	Missing Cases	80	8.8	
	Total	913	100.0	
Unselected Cases	0	.0	
Total	913	100.0	

a. If weight is in effect, see classification table for the total number of cases.	

Dependent Variable Encoding	
Original Value	Internal Value	
No	0	
Yes	1	

Categorical Variables Codings	
	Frequency	Parameter coding	
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	
What is your household income level?	Less than $10,000	46	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	
	$10,000 to $14,999	26	1.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	
	$15,000 to $24,999	32	.000	1.000	.000	.000	.000	.000	.000	.000	.000	.000	
	$25,000 to $34,999	66	.000	.000	1.000	.000	.000	.000	.000	.000	.000	.000	
	$35,000 to $49,999	54	.000	.000	.000	1.000	.000	.000	.000	.000	.000	.000	
	$50,000 to $74,999	88	.000	.000	.000	.000	1.000	.000	.000	.000	.000	.000	
	$75,000 to $99,999	68	.000	.000	.000	.000	.000	1.000	.000	.000	.000	.000	
	$100,000 to $149,999	89	.000	.000	.000	.000	.000	.000	1.000	.000	.000	.000	
	$150,000 to $199,999	28	.000	.000	.000	.000	.000	.000	.000	1.000	.000	.000	
	$200,000 or more	48	.000	.000	.000	.000	.000	.000	.000	.000	1.000	.000	
	Prefer not to respond	288	.000	.000	.000	.000	.000	.000	.000	.000	.000	1.000	
What is your age? (Binned)	<= 25.00	136	.000	.000	.000	.000	.000						
	26.00 - 40.00	202	1.000	.000	.000	.000	.000						
	41.00 - 55.00	226	.000	1.000	.000	.000	.000						
	56.00 - 65.00	131	.000	.000	1.000	.000	.000						
	66.00 - 75.00	93	.000	.000	.000	1.000	.000						
	76.00+	45	.000	.000	.000	.000	1.000						
In General, would you say your health is:	Excellent	95	.000	.000	.000	.000							
	Very Good	235	1.000	.000	.000	.000							
	Good	256	.000	1.000	.000	.000							
	Fair	165	.000	.000	1.000	.000							
	Poor	82	.000	.000	.000	1.000							
What level of education have you achieved?	Some high school	35	.000	.000	.000	.000							
	High school graduate	137	1.000	.000	.000	.000							
	Some college	285	.000	1.000	.000	.000							
	College graduate	234	.000	.000	1.000	.000							
	Post graduate degree (Master's, PhD, MD, JD, MSW)	142	.000	.000	.000	1.000							
Numeracyscore (Binned)	Low numeracy	98	.000	.000									
	Medium numeracy	245	1.000	.000									
	High numeracy	490	.000	1.000									
TbiRisk	0.1	417	.000										
	1	416	1.000										
TbiCost	0	419	.000										
	100	414	1.000										
whitevsnonwhite	0	152	.000										
	white	681	1.000										
med indicator	.00	611	.000										
	1.00	222	1.000										
male	.00	488	.000										
	1.00	345	1.000										
Have you had a head injury before that required a visit to the hospital or emergency department?	Yes	181	.000										
	No	652	1.000										
Have you had a heart attack before?	0	791	.000										
	Yes	42	1.000										
Do you have atrial fibrillation? (An abnormal heart rhythm)	0	767	.000										
	Yes	66	1.000										
Do you have diabetes?	0	699	.000										
	Yes	134	1.000										
Do you have a history of cancer	0	722	.000										
	Yes	111	1.000										
Do you have high blood pressure or do you take medication for high blood pressure?	0	578	.000										
	Yes	255	1.000										
TbiBenefit	0.1	412	.000										
	1	421	1.000										

Block 0: Beginning Block

Classification Tablea,b	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 0	Would you agree to have this diagnostic test performed given the above information.	No	0	377	.0	
		Yes	0	456	100.0	
	Overall Percentage			54.7	

a. Constant is included in the model.	
b. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	
Step 0	Constant	.190	.070	7.470	1	.006	1.210	

Variables not in the Equation	
	Score	df	Sig.	
Step 0	Variables	TbiBenefit(1)	5.961	1	.015	
		TbiRisk(1)	8.326	1	.004	
		TbiCost(1)	13.747	1	.000	
		Numeracyscore (Binned)	8.630	2	.013	
		Numeracyscore (Binned)(1)	8.321	1	.004	
		Numeracyscore (Binned)(2)	6.652	1	.010	
		What is your household income level?	12.122	10	.277	
		What is your household income level?(1)	5.330	1	.021	
		What is your household income level?(2)	2.636	1	.104	
		What is your household income level?(3)	.232	1	.630	
		What is your household income level?(4)	.476	1	.490	
		What is your household income level?(5)	.035	1	.851	
		What is your household income level?(6)	.003	1	.954	
		What is your household income level?(7)	1.131	1	.287	
		What is your household income level?(8)	1.652	1	.199	
		What is your household income level?(9)	.958	1	.328	
		What is your household income level?(10)	.151	1	.697	
		What level of education have you achieved?	7.452	4	.114	
		What level of education have you achieved?(1)	5.081	1	.024	
		What level of education have you achieved?(2)	.085	1	.771	
		What level of education have you achieved?(3)	2.953	1	.086	
		What level of education have you achieved?(4)	.768	1	.381	
		What is your age? (Binned)	11.298	5	.046	
		What is your age? (Binned)(1)	1.515	1	.218	
		What is your age? (Binned)(2)	2.592	1	.107	
		What is your age? (Binned)(3)	5.015	1	.025	
		What is your age? (Binned)(4)	3.198	1	.074	
		What is your age? (Binned)(5)	1.074	1	.300	
		whitevsnonwhite(1)	6.179	1	.013	
		med indicator(1)	1.404	1	.236	
		male(1)	.000	1	.984	
		Do you have a history of cancer(1)	.130	1	.718	
		Do you have high blood pressure or do you take medication for high blood pressure?(1)	1.613	1	.204	
		Do you have diabetes?(1)	7.700	1	.006	
		Do you have atrial fibrillation? (An abnormal heart rhythm)(1)	.232	1	.630	
		Have you had a heart attack before?(1)	.916	1	.339	
		Have you had a head injury before that required a visit to the hospital or emergency department?(1)	2.802	1	.094	
		In General, would you say your health is:	17.442	4	.002	
		In General, would you say your health is:(1)	.516	1	.473	
		In General, would you say your health is:(2)	.858	1	.354	
		In General, would you say your health is:(3)	8.483	1	.004	
		In General, would you say your health is:(4)	2.761	1	.097	
	Overall Statistics	81.105	37	.000	

Block 1: Method = Enter

Omnibus Tests of Model Coefficients	
	Chi-square	df	Sig.	
Step 1	Step	85.660	37	.000	
	Block	85.660	37	.000	
	Model	85.660	37	.000	

Model Summary	
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square	
1	1061.620a	.098	.131	

a. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.	

Hosmer and Lemeshow Test	
Step	Chi-square	df	Sig.	
1	10.639	8	.223	

Contingency Table for Hosmer and Lemeshow Test	
	Would you agree to have this diagnostic test performed given the above information. = No	Would you agree to have this diagnostic test performed given the above information. = Yes	Total	
	Observed	Expected	Observed	Expected		
Step 1	1	58	59.641	25	23.359	83	
	2	53	51.948	30	31.052	83	
	3	48	47.023	35	35.977	83	
	4	44	43.240	39	39.760	83	
	5	42	39.210	41	43.790	83	
	6	30	36.031	53	46.969	83	
	7	40	32.207	43	50.793	83	
	8	22	28.309	61	54.691	83	
	9	19	23.320	64	59.680	83	
	10	21	16.072	65	69.928	86	

Classification Tablea	
	Observed	Predicted	
		Would you agree to have this diagnostic test performed given the above information.	Percentage Correct	
		No	Yes		
Step 1	Would you agree to have this diagnostic test performed given the above information.	No	198	179	52.5	
		Yes	127	329	72.1	
	Overall Percentage			63.3	

a. The cut value is .500	

Variables in the Equation	
	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I.for EXP(B)	
							Lower	Upper	
Step 1a	TbiBenefit(1)	.454	.151	9.053	1	.003	1.575	1.172	2.118	
	TbiRisk(1)	-.391	.150	6.769	1	.009	.676	.504	.908	
	TbiCost(1)	-.509	.149	11.662	1	.001	.601	.449	.805	
	Numeracyscore (Binned)			2.869	2	.238				
	Numeracyscore (Binned)(1)	.339	.263	1.657	1	.198	1.404	.838	2.352	
	Numeracyscore (Binned)(2)	.051	.248	.042	1	.837	1.052	.647	1.711	
	What is your household income level?			7.143	10	.712				
	What is your household income level?(1)	.725	.578	1.575	1	.209	2.066	.665	6.412	
	What is your household income level?(2)	.760	.510	2.218	1	.136	2.138	.787	5.809	
	What is your household income level?(3)	.361	.422	.732	1	.392	1.435	.627	3.284	
	What is your household income level?(4)	.325	.441	.545	1	.460	1.385	.584	3.285	
	What is your household income level?(5)	.233	.399	.340	1	.560	1.262	.578	2.758	
	What is your household income level?(6)	.331	.419	.622	1	.430	1.392	.612	3.166	
	What is your household income level?(7)	.070	.410	.029	1	.865	1.072	.480	2.397	
	What is your household income level?(8)	-.237	.528	.202	1	.653	.789	.280	2.220	
	What is your household income level?(9)	.192	.463	.171	1	.679	1.211	.489	3.002	
	What is your household income level?(10)	.036	.344	.011	1	.917	1.037	.528	2.033	
	What level of education have you achieved?			2.013	4	.733				
	What level of education have you achieved?(1)	.422	.421	1.003	1	.317	1.525	.668	3.480	
	What level of education have you achieved?(2)	.127	.407	.097	1	.756	1.135	.511	2.519	
	What level of education have you achieved?(3)	.154	.426	.130	1	.718	1.166	.506	2.688	
	What level of education have you achieved?(4)	.204	.449	.206	1	.650	1.226	.508	2.955	
	What is your age? (Binned)			10.046	5	.074				
	What is your age? (Binned)(1)	.012	.248	.002	1	.962	1.012	.622	1.647	
	What is your age? (Binned)(2)	.369	.252	2.140	1	.143	1.446	.882	2.368	
	What is your age? (Binned)(3)	-.186	.296	.393	1	.531	.831	.465	1.484	
	What is your age? (Binned)(4)	.541	.344	2.464	1	.116	1.717	.874	3.372	
	What is your age? (Binned)(5)	.426	.421	1.024	1	.312	1.531	.671	3.491	
	whitevsnonwhite(1)	-.468	.203	5.311	1	.021	.626	.420	.932	
	med indicator(1)	-.192	.177	1.172	1	.279	.825	.583	1.168	
	male(1)	.004	.159	.001	1	.979	1.004	.736	1.371	
	Do you have a history of cancer(1)	-.328	.235	1.946	1	.163	.720	.454	1.142	
	Do you have high blood pressure or do you take medication for high blood pressure?(1)	-.126	.198	.406	1	.524	.882	.598	1.299	
	Do you have diabetes?(1)	.346	.233	2.190	1	.139	1.413	.894	2.233	
	Do you have atrial fibrillation? (An abnormal heart rhythm)(1)	-.049	.296	.027	1	.869	.952	.533	1.703	
	Have you had a heart attack before?(1)	-.011	.379	.001	1	.976	.989	.470	2.078	
	Have you had a head injury before that required a visit to the hospital or emergency department?(1)	-.344	.185	3.445	1	.063	.709	.493	1.019	
	In General, would you say your health is:			10.500	4	.033				
	In General, would you say your health is:(1)	.488	.266	3.375	1	.066	1.630	.968	2.743	
	In General, would you say your health is:(2)	.394	.268	2.169	1	.141	1.483	.878	2.505	
	In General, would you say your health is:(3)	.890	.305	8.532	1	.003	2.436	1.340	4.428	
	In General, would you say your health is:(4)	.868	.358	5.884	1	.015	2.382	1.181	4.804	
	Constant	-.006	.596	.000	1	.992	.994			

a. Variable(s) entered on step 1: TbiBenefit, TbiRisk, TbiCost, Numeracyscore (Binned), What is your household income level?, What level of education have you achieved?, What is your age? (Binned), whitevsnonwhite, med indicator, male, Do you have a history of cancer, Do you have high blood pressure or do you take medication for high blood pressure?, Do you have diabetes?, Do you have atrial fibrillation? (An abnormal heart rhythm), Have you had a heart attack before?, Have you had a head injury before that required a visit to the hospital or emergency department?, In General, would you say your health is:.	

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary numeracy_24 numeracy_25 numeracy_26
    educ_2 educ_3 educ_4 educ_5 educ_6 male medprof whitevsnonwhite Q19 Q20 Q21 Q22 Q23 Q56.0 health_8
    health_9 health_10 health_11 health_12.

Regression

Notes	
Output Created	23-APR-2019 17:39:22	
Comments		
Input	Data	K:\[Emergency Medicine] Diagnostic Testing Research\Fall 2018 [Jessica]\Rahul2016DiagnosticAccuracyUMHS2019Apr23.sav	
	Active Dataset	DataSet1	
	Filter	<none>	
	Weight	<none>	
	Split File	<none>	
	N of Rows in Working Data File	913	
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.	
	Cases Used	Statistics are based on cases with no missing values for any variable used.	
Syntax	REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT OutcomeTBI
  /METHOD=ENTER BenefitTBIbinary riskTBIbinary CostTBIbinary numeracy_24 numeracy_25 numeracy_26
    educ_2 educ_3 educ_4 educ_5 educ_6 male medprof whitevsnonwhite Q19 Q20 Q21 Q22 Q23 Q56.0 health_8
    health_9 health_10 health_11 health_12.	
Resources	Processor Time	00:00:00.05	
	Elapsed Time	00:00:00.06	
	Memory Required	54416 bytes	
	Additional Memory Required for Residual Plots	0 bytes	

Variables Entered/Removeda	
Model	Variables Entered	Variables Removed	Method	
1	Q31=5.0, High risk versus low risk, whitevsnonwhite, Numeracybinned=1.0, Incentive versus not, Q24=2.0, male, Indicator varibale for benefit, Q31=1.0, Have you had a head injury before that required a visit to the hospital or emergency department?, Do you have a history of cancer, Do you have atrial fibrillation? (An abnormal heart rhythm), Q24=1.0, Do you have diabetes?, Q24=5.0, med indicator, Have you had a heart attack before?, Q31=3.0, Numeracybinned=2.0, Do you have high blood pressure or do you take medication for high blood pressure?, Q24=4.0, Q31=4.0b	.	Enter	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Tolerance = .000 limit reached.	

Model Summary	
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	
1	.281a	.079	.054	.484	

a. Predictors: (Constant), Q31=5.0, High risk versus low risk, whitevsnonwhite, Numeracybinned=1.0, Incentive versus not, Q24=2.0, male, Indicator varibale for benefit, Q31=1.0, Have you had a head injury before that required a visit to the hospital or emergency department?, Do you have a history of cancer, Do you have atrial fibrillation? (An abnormal heart rhythm), Q24=1.0, Do you have diabetes?, Q24=5.0, med indicator, Have you had a heart attack before?, Q31=3.0, Numeracybinned=2.0, Do you have high blood pressure or do you take medication for high blood pressure?, Q24=4.0, Q31=4.0	

ANOVAa	
Model	Sum of Squares	df	Mean Square	F	Sig.	
1	Regression	16.364	22	.744	3.171	.000b	
	Residual	190.722	813	.235			
	Total	207.086	835				

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors: (Constant), Q31=5.0, High risk versus low risk, whitevsnonwhite, Numeracybinned=1.0, Incentive versus not, Q24=2.0, male, Indicator varibale for benefit, Q31=1.0, Have you had a head injury before that required a visit to the hospital or emergency department?, Do you have a history of cancer, Do you have atrial fibrillation? (An abnormal heart rhythm), Q24=1.0, Do you have diabetes?, Q24=5.0, med indicator, Have you had a heart attack before?, Q31=3.0, Numeracybinned=2.0, Do you have high blood pressure or do you take medication for high blood pressure?, Q24=4.0, Q31=4.0	

Coefficientsa	
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	
	B	Std. Error	Beta			
1	(Constant)	.799	.102		7.813	.000	
	Indicator varibale for benefit	.100	.034	.100	2.931	.003	
	High risk versus low risk	-.096	.034	-.096	-2.830	.005	
	Incentive versus not	-.122	.034	-.122	-3.589	.000	
	Numeracybinned=1.0	-.021	.055	-.014	-.378	.705	
	Numeracybinned=2.0	.062	.041	.057	1.501	.134	
	Q24=1.0	-.011	.090	-.004	-.119	.905	
	Q24=2.0	.081	.052	.060	1.575	.116	
	Q24=4.0	.010	.044	.009	.227	.820	
	Q24=5.0	.020	.052	.015	.390	.696	
	male	.000	.036	.000	-.012	.990	
	med indicator	-.042	.040	-.037	-1.040	.298	
	whitevsnonwhite	-.104	.044	-.081	-2.342	.019	
	Do you have a history of cancer	-.052	.051	-.036	-1.023	.307	
	Do you have high blood pressure or do you take medication for high blood pressure?	-.012	.041	-.011	-.292	.770	
	Do you have diabetes?	.076	.051	.056	1.496	.135	
	Do you have atrial fibrillation? (An abnormal heart rhythm)	-.002	.065	-.001	-.036	.972	
	Have you had a heart attack before?	.007	.083	.003	.086	.932	
	Have you had a head injury before that required a visit to the hospital or emergency department?	-.080	.042	-.066	-1.922	.055	
	Q31=1.0	-.110	.060	-.070	-1.843	.066	
	Q31=3.0	-.018	.045	-.017	-.403	.687	
	Q31=4.0	.098	.053	.078	1.837	.067	
	Q31=5.0	.077	.067	.046	1.142	.254	

Coefficientsa	
Model	Collinearity Statistics	
	Tolerance	VIF	
1	(Constant)			
	Indicator varibale for benefit	.970	1.031	
	High risk versus low risk	.984	1.017	
	Incentive versus not	.978	1.023	
	Numeracybinned=1.0	.870	1.149	
	Numeracybinned=2.0	.787	1.271	
	Q24=1.0	.873	1.146	
	Q24=2.0	.771	1.296	
	Q24=4.0	.703	1.423	
	Q24=5.0	.732	1.365	
	male	.899	1.112	
	med indicator	.886	1.128	
	whitevsnonwhite	.953	1.049	
	Do you have a history of cancer	.939	1.065	
	Do you have high blood pressure or do you take medication for high blood pressure?	.770	1.299	
	Do you have diabetes?	.798	1.254	
	Do you have atrial fibrillation? (An abnormal heart rhythm)	.905	1.105	
	Have you had a heart attack before?	.855	1.169	
	Have you had a head injury before that required a visit to the hospital or emergency department?	.957	1.044	
	Q31=1.0	.782	1.279	
	Q31=3.0	.660	1.515	
	Q31=4.0	.621	1.610	
	Q31=5.0	.695	1.438	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	

Excluded Variablesa	
Model	Beta In	t	Sig.	Partial Correlation	Collinearity Statistics	
					Tolerance	
1	Numeracybinned=3.0	.b	.	.	.	.000	
	Q24=3.0	.b	.	.	.	.000	
	Q31=2.0	.b	.	.	.	.000	

Excluded Variablesa	
Model	Collinearity Statistics	
	VIF	Minimum Tolerance	
1	Numeracybinned=3.0	.	.000	
	Q24=3.0	.	.000	
	Q31=2.0	.	.000	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.	
b. Predictors in the Model: (Constant), Q31=5.0, High risk versus low risk, whitevsnonwhite, Numeracybinned=1.0, Incentive versus not, Q24=2.0, male, Indicator varibale for benefit, Q31=1.0, Have you had a head injury before that required a visit to the hospital or emergency department?, Do you have a history of cancer, Do you have atrial fibrillation? (An abnormal heart rhythm), Q24=1.0, Do you have diabetes?, Q24=5.0, med indicator, Have you had a heart attack before?, Q31=3.0, Numeracybinned=2.0, Do you have high blood pressure or do you take medication for high blood pressure?, Q24=4.0, Q31=4.0	

Collinearity Diagnosticsa	
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions	
				(Constant)	Indicator varibale for benefit	High risk versus low risk	
1	1	7.942	1.000	.00	.00	.00	
	2	1.768	2.120	.00	.00	.00	
	3	1.166	2.610	.00	.00	.00	
	4	1.114	2.670	.00	.00	.00	
	5	1.084	2.707	.00	.00	.00	
	6	.991	2.830	.00	.00	.00	
	7	.949	2.892	.00	.00	.00	
	8	.942	2.903	.00	.00	.00	
	9	.882	3.000	.00	.00	.00	
	10	.776	3.198	.00	.00	.00	
	11	.729	3.300	.00	.00	.01	
	12	.692	3.389	.00	.00	.00	
	13	.641	3.521	.00	.05	.01	
	14	.515	3.929	.00	.33	.01	
	15	.506	3.963	.00	.04	.35	
	16	.458	4.164	.00	.00	.38	
	17	.445	4.225	.00	.16	.12	
	18	.419	4.351	.00	.19	.03	
	19	.367	4.654	.00	.11	.00	
	20	.279	5.340	.00	.03	.00	
	21	.201	6.279	.00	.05	.03	
	22	.114	8.337	.03	.01	.02	
	23	.019	20.507	.96	.01	.02	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Incentive versus not	Numeracybinned=1.0	Numeracybinned=2.0	Q24=1.0	Q24=2.0	
1	1	.00	.00	.00	.00	.00	
	2	.00	.01	.00	.02	.01	
	3	.00	.01	.09	.02	.07	
	4	.00	.20	.08	.01	.03	
	5	.00	.02	.00	.29	.03	
	6	.00	.07	.01	.14	.11	
	7	.00	.00	.01	.08	.07	
	8	.01	.04	.00	.05	.01	
	9	.00	.02	.00	.04	.01	
	10	.00	.18	.01	.00	.01	
	11	.00	.00	.01	.00	.00	
	12	.02	.08	.02	.01	.06	
	13	.00	.06	.00	.05	.03	
	14	.44	.04	.04	.00	.01	
	15	.03	.02	.08	.03	.09	
	16	.00	.03	.12	.02	.01	
	17	.25	.00	.00	.00	.00	
	18	.01	.12	.27	.09	.10	
	19	.13	.01	.03	.10	.18	
	20	.06	.07	.19	.01	.05	
	21	.00	.00	.00	.00	.08	
	22	.02	.01	.03	.01	.03	
	23	.01	.01	.02	.01	.00	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Q24=4.0	Q24=5.0	male	med indicator	whitevsnonwhite	Do you have a history of cancer	
1	1	.00	.00	.00	.00	.00	.00	
	2	.02	.02	.00	.01	.00	.01	
	3	.00	.08	.00	.00	.00	.01	
	4	.06	.06	.02	.04	.00	.00	
	5	.01	.02	.01	.00	.00	.01	
	6	.06	.02	.00	.04	.00	.04	
	7	.01	.01	.04	.06	.00	.00	
	8	.11	.14	.00	.01	.00	.01	
	9	.00	.00	.01	.04	.00	.35	
	10	.00	.01	.02	.07	.00	.26	
	11	.00	.00	.02	.01	.00	.09	
	12	.00	.00	.03	.12	.00	.01	
	13	.00	.04	.07	.11	.00	.17	
	14	.00	.02	.04	.00	.00	.00	
	15	.00	.00	.15	.11	.00	.00	
	16	.02	.03	.00	.01	.00	.00	
	17	.00	.00	.30	.17	.00	.00	
	18	.00	.01	.03	.01	.00	.00	
	19	.25	.24	.19	.15	.02	.02	
	20	.15	.14	.04	.00	.03	.01	
	21	.23	.13	.01	.01	.41	.00	
	22	.05	.03	.02	.00	.43	.00	
	23	.01	.01	.01	.02	.10	.00	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Do you have high blood pressure or do you take medication for high blood pressure?	Do you have diabetes?	Do you have atrial fibrillation? (An abnormal heart rhythm)	Have you had a heart attack before?	Have you had a head injury before that required a visit to the hospital or emergency department?	
1	1	.00	.00	.00	.00	.00	
	2	.02	.04	.03	.07	.00	
	3	.00	.01	.04	.04	.00	
	4	.00	.00	.00	.00	.00	
	5	.01	.00	.00	.00	.00	
	6	.00	.00	.07	.00	.00	
	7	.02	.06	.00	.01	.00	
	8	.00	.00	.02	.01	.00	
	9	.00	.01	.17	.14	.00	
	10	.00	.03	.00	.19	.00	
	11	.03	.18	.51	.11	.00	
	12	.03	.18	.10	.29	.00	
	13	.00	.04	.02	.00	.00	
	14	.00	.01	.00	.03	.00	
	15	.08	.06	.00	.06	.00	
	16	.41	.08	.01	.04	.00	
	17	.11	.02	.00	.01	.00	
	18	.22	.26	.01	.01	.00	
	19	.01	.00	.00	.00	.00	
	20	.03	.02	.00	.00	.00	
	21	.01	.00	.00	.00	.01	
	22	.00	.00	.00	.00	.21	
	23	.00	.00	.00	.00	.78	

Collinearity Diagnosticsa	
Model	Dimension	Variance Proportions	
		Q31=1.0	Q31=3.0	Q31=4.0	Q31=5.0	
1	1	.00	.00	.00	.00	
	2	.04	.01	.02	.03	
	3	.07	.03	.02	.06	
	4	.03	.02	.00	.01	
	5	.04	.03	.08	.05	
	6	.02	.00	.00	.10	
	7	.07	.00	.11	.10	
	8	.10	.09	.03	.00	
	9	.00	.03	.02	.01	
	10	.00	.03	.00	.11	
	11	.03	.01	.01	.01	
	12	.03	.01	.01	.00	
	13	.22	.03	.03	.07	
	14	.01	.00	.00	.00	
	15	.00	.00	.00	.00	
	16	.00	.01	.00	.01	
	17	.03	.00	.00	.01	
	18	.02	.02	.01	.00	
	19	.02	.00	.00	.00	
	20	.15	.31	.34	.21	
	21	.07	.25	.22	.15	
	22	.03	.08	.07	.02	
	23	.02	.04	.02	.05	

a. Dependent Variable: Would you agree to have this diagnostic test performed given the above information.