*this opens the data file (if you download the datafile you'll need to change the directory) . . use "C:\Users\Owner\Desktop\Papers\MJ_UPPS\MJdta2.dta", clear . . *recodes MJ problems from no = 2 to no = 0 . . foreach var of varlist macq1-macq50 { 2. recode `var' 2=0 3. } (macq1: 753 changes made) (macq2: 799 changes made) (macq3: 771 changes made) (macq4: 724 changes made) (macq5: 651 changes made) (macq6: 793 changes made) (macq7: 792 changes made) (macq8: 794 changes made) (macq9: 817 changes made) (macq10: 781 changes made) (macq11: 803 changes made) (macq12: 797 changes made) (macq13: 818 changes made) (macq14: 791 changes made) (macq15: 800 changes made) (macq16: 801 changes made) (macq17: 814 changes made) (macq18: 768 changes made) (macq19: 818 changes made) (macq20: 778 changes made) (macq21: 782 changes made) (macq22: 803 changes made) (macq23: 797 changes made) (macq24: 817 changes made) (macq25: 787 changes made) (macq26: 816 changes made) (macq27: 804 changes made) (macq28: 771 changes made) (macq29: 804 changes made) (macq30: 801 changes made) (macq31: 815 changes made) (macq32: 803 changes made) (macq33: 817 changes made) (macq34: 809 changes made) (macq35: 816 changes made) (macq36: 805 changes made) (macq37: 794 changes made) (macq38: 814 changes made) (macq39: 782 changes made) (macq40: 807 changes made) (macq41: 812 changes made) (macq42: 807 changes made) (macq43: 802 changes made) (macq44: 818 changes made) (macq45: 786 changes made) (macq46: 775 changes made) (macq47: 814 changes made) (macq48: 803 changes made) (macq49: 751 changes made) (macq50: 782 changes made) . . . *labels the race variable . . lab define race 1 "white" 2 "black" 3 "asian" 4 "native american" 5 "pacific islander" 6 "other" . lab val race race . . *recodes and labels the sex variable . . recode sex 2=0 (sex: 794 changes made) . lab define sex 1 "Men" 0 "Women" . lab val sex sex . . *tags missing data so it can be excluded . . egen missnew = rowmiss(macq1-macq50) . gen missing = 1 if missnew==50 (821 missing values generated) . . drop if missing==1 (419 observations deleted) . . *generates positive & negative affect variables . . alpha interested excited strong enthusiastic /// > proud alert inspired determined attentive /// > active, g(posaff) Test scale = mean(unstandardized items) Average interitem covariance: .4578604 Number of items in the scale: 10 Scale reliability coefficient: 0.8891 . . alpha distressed upset guilty scared hostile /// > irritable ashamed nervous jittery afraid, /// > g(negaff) Test scale = mean(unstandardized items) Average interitem covariance: .3978426 Number of items in the scale: 10 Scale reliability coefficient: 0.8893 . . . . *Log transforms 6 month MJ use variable which . *was highly skewed . . sum mj6mo, det How often have you used Marijuana in the last 6 months? ------------------------------------------------------------- Percentiles Smallest 1% 1 1 5% 1 1 10% 1 1 Obs 821 25% 1 1 Sum of Wgt. 821 50% 1 Mean 1.650426 Largest Std. Dev. 1.508784 75% 1 8 90% 3 8 Variance 2.27643 95% 5 8 Skewness 2.690773 99% 8 8 Kurtosis 9.831911 . gen log6mo = ln(mj6mo) . sum log6mo, det log6mo ------------------------------------------------------------- Percentiles Smallest 1% 0 0 5% 0 0 10% 0 0 Obs 821 25% 0 0 Sum of Wgt. 821 50% 0 Mean .280941 Largest Std. Dev. .5658719 75% 0 2.079442 90% 1.098612 2.079442 Variance .320211 95% 1.609438 2.079442 Skewness 1.84279 99% 2.079442 2.079442 Kurtosis 5.098609 . . . *displays sample characteristics for the full sample . . tab sex Are you | male or | female? | Freq. Percent Cum. ------------+----------------------------------- Women | 528 64.31 64.31 Men | 293 35.69 100.00 ------------+----------------------------------- Total | 821 100.00 . tab race What is your | race? | Freq. Percent Cum. -----------------+----------------------------------- white | 756 92.08 92.08 black | 10 1.22 93.30 asian | 31 3.78 97.08 native american | 4 0.49 97.56 pacific islander | 1 0.12 97.69 other | 19 2.31 100.00 -----------------+----------------------------------- Total | 821 100.00 . sum age, det What is your age? ------------------------------------------------------------- Percentiles Smallest 1% 18 2 5% 18 18 10% 18 18 Obs 821 25% 19 18 Sum of Wgt. 821 50% 19 Mean 20.11084 Largest Std. Dev. 2.434737 75% 21 30 90% 23 30 Variance 5.927943 95% 25 33 Skewness 1.341541 99% 30 33 Kurtosis 11.10216 . . . *this imputes missing data for the affect lability scales -sf. . *note, there was no missing, however, this is done as a normal . *part of measure coding as some measures are reversed scored. . *this allows for us to recode items without changing original . *items. . . foreach var of varlist als1-als18 { 2. impute `var' als1-als18, gen(i_`var') 3. gen ri_`var' = round(i_`var') 4. drop i_`var' 5. } 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed . . *this generates the three als scales and the final . *the als total score . *cronbachs alpha for the als total score . *als score comprised from the three scales . . egen alsmiss = rowmiss(als1-als18) . egen alsad = rowtotal(ri_als1 ri_als3 ri_als5 ri_als6 ri_als7) . alpha ri_als1 ri_als3 ri_als5 ri_als6 ri_als7 Test scale = mean(unstandardized items) Average interitem covariance: .5890739 Number of items in the scale: 5 Scale reliability coefficient: 0.8944 . egen alsde = rowtotal(ri_als2 ri_als10 ri_als12 ri_als13 ri_als15 ri_als16 ri_als17 ri_als18) . alpha ri_als2 ri_als10 ri_als12 ri_als13 ri_als15 ri_als16 ri_als17 ri_als18 Test scale = mean(unstandardized items) Average interitem covariance: .4847282 Number of items in the scale: 8 Scale reliability coefficient: 0.8945 . egen alsang = rowtotal(ri_als4 ri_als8 ri_als9 ri_als11 ri_als14) . alpha ri_als4 ri_als8 ri_als9 ri_als11 ri_als14 Test scale = mean(unstandardized items) Average interitem covariance: .5434025 Number of items in the scale: 5 Scale reliability coefficient: 0.8985 . egen alstot = rowtotal(ri_als1-ri_als18) if alsmiss<9 . alpha ri_als1-ri_als18 if alsmiss<9 Test scale = mean(unstandardized items) Average interitem covariance: .4400955 Number of items in the scale: 18 Scale reliability coefficient: 0.9434 . . alpha alsang alsad alsde if missing~=1, g(ALS) std Test scale = mean(standardized items) Average interitem correlation: 0.6789 Number of items in the scale: 3 Scale reliability coefficient: 0.8638 . . alpha ri_als1-ri_als18, g(alsall) Test scale = mean(unstandardized items) Average interitem covariance: .4400955 Number of items in the scale: 18 Scale reliability coefficient: 0.9434 . . *this imputes missing data for the distress tolerance scale. . *note, there was no missing, however, this is done as a normal . *part of measure coding as some measures are reversed scored. . *this allows for us to recode items without changing original . *items. . . foreach var of varlist dts1-dts15 { 2. impute `var' dts1-dts15, gen(i_`var') 3. gen ri_`var' = round(i_`var') 4. drop i_`var' 5. } 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed . . * this creates a new item from the dts for reverse scoring . . gen rev_ri_dts6 = ri_dts6 . . * this recodes the above item . . recode rev_ri_dts6 1=5 2=4 4=2 5=1 (rev_ri_dts6: 648 changes made) . . *computes a missing score for the dts and finally . *converts the dts into a standardized score. . . egen dtsmiss = rowmiss(dts1-dts15) . alpha ri_dts1-ri_dts15 if dtsmiss<8, g(dtsall) std Test scale = mean(standardized items) Reversed item: ri_dts6 Average interitem correlation: 0.4747 Number of items in the scale: 15 Scale reliability coefficient: 0.9313 . . * this creates a total score for the dts . . egen dtstot = rowtotal(ri_dts1-ri_dts5 rev_ri_dts6 /// > ri_dts7-ri_dts15) if dtsmiss<8 . . * gives cronbachs alpha for dts total score . . alpha ri_dts1-ri_dts5 rev_ri_dts6 /// > ri_dts7-ri_dts15 Test scale = mean(unstandardized items) Average interitem covariance: .7448914 Number of items in the scale: 15 Scale reliability coefficient: 0.9323 . . *computes the dts subscales . . egen dtstol = rowtotal(ri_dts1 ri_dts3 ri_dts5) . alpha ri_dts1 ri_dts3 ri_dts5 if missing~=1 Test scale = mean(unstandardized items) Average interitem covariance: .8626274 Number of items in the scale: 3 Scale reliability coefficient: 0.8042 . . egen dtsapp = rowtotal(rev_ri_dts6 ri_dts7 ri_dts9 ri_dts10 ri_dts12 ri_dts12) . alpha rev_ri_dts6 ri_dts7 ri_dts9 ri_dts10 ri_dts12 ri_dts12 if missing~=1 Test scale = mean(unstandardized items) Average interitem covariance: .6990407 Number of items in the scale: 5 Scale reliability coefficient: 0.8111 . . egen dtsabs = rowtotal(ri_dts2 ri_dts4 ri_dts15) . alpha ri_dts2 ri_dts4 ri_dts15 if missing~=1 Test scale = mean(unstandardized items) Average interitem covariance: 1.102716 Number of items in the scale: 3 Scale reliability coefficient: 0.8575 . . egen dtsreg = rowtotal(ri_dts8 ri_dts13 ri_dts14) . alpha ri_dts8 ri_dts13 ri_dts14 if missing~=1 Test scale = mean(unstandardized items) Average interitem covariance: .9127452 Number of items in the scale: 3 Scale reliability coefficient: 0.8189 . . *computes a standard score for the dts from the four subscale totals . . alpha dtstol dtsapp dtsabs dtsreg if missing~=1, g(DTS) std Test scale = mean(standardized items) Average interitem correlation: 0.6661 Number of items in the scale: 4 Scale reliability coefficient: 0.8886 . . . *imputes data for the upps-p. note that there was . *no missing data imputed for the current analysis. this . *just allows us to use code we have wirtten for recoding . *upps-p items. . . foreach var of varlist upps1-upps20 { 2. impute `var' upps1-upps20, gen(i_`var') 3. gen ri_`var' = round(i_`var') 4. drop i_`var' 5. } 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) . foreach var of varlist upps21-upps40 { 2. impute `var' upps21-upps40, gen(i_`var') 3. gen ri_`var' = round(i_`var') 4. drop i_`var' 5. } 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) 0.12% (1) observations imputed (1 missing value generated) . foreach var of varlist upps41-upps59 { 2. impute `var' upps41-upps59, gen(i_`var') 3. gen ri_`var' = round(i_`var') 4. drop i_`var' 5. } 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.12% (1) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed 0.00% (0) observations imputed . *creates variables for reverse coding. . . foreach var of varlist ri_upps2 ri_upps7 ri_upps12 ri_upps17 ri_upps22 /// > ri_upps29 ri_upps34 ri_upps39 ri_upps44 ri_upps51 ri_upps58 ri_upps9 /// > ri_upps47 ri_upps3 ri_upps8 ri_upps13 ri_upps18 ri_upps23 ri_upps26 /// > ri_upps31 ri_upps36 ri_upps41 ri_upps46 ri_upps52 ri_upps56 ri_upps5 /// > ri_upps10 ri_upps15 ri_upps20 ri_upps25 ri_upps30 ri_upps35 ri_upps40 /// > ri_upps45 ri_upps50 ri_upps53 ri_upps55 ri_upps57 ri_upps59 { 2. gen rev_`var' = `var' 3. } (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) (1 missing value generated) . . *reverse codes the above variables . . foreach var of varlist rev_ri_upps2- rev_ri_upps59 { 2. recode `var' 1=4 2=3 3=2 4=1 3. } (rev_ri_upps2: 820 changes made) (rev_ri_upps7: 820 changes made) (rev_ri_upps12: 820 changes made) (rev_ri_upps17: 820 changes made) (rev_ri_upps22: 820 changes made) (rev_ri_upps29: 820 changes made) (rev_ri_upps34: 820 changes made) (rev_ri_upps39: 820 changes made) (rev_ri_upps44: 821 changes made) (rev_ri_upps51: 821 changes made) (rev_ri_upps58: 821 changes made) (rev_ri_upps9: 820 changes made) (rev_ri_upps47: 821 changes made) (rev_ri_upps3: 820 changes made) (rev_ri_upps8: 820 changes made) (rev_ri_upps13: 820 changes made) (rev_ri_upps18: 820 changes made) (rev_ri_upps23: 820 changes made) (rev_ri_upps26: 820 changes made) (rev_ri_upps31: 820 changes made) (rev_ri_upps36: 820 changes made) (rev_ri_upps41: 821 changes made) (rev_ri_upps46: 821 changes made) (rev_ri_upps52: 821 changes made) (rev_ri_upps56: 821 changes made) (rev_ri_upps5: 820 changes made) (rev_ri_upps10: 820 changes made) (rev_ri_upps15: 820 changes made) (rev_ri_upps20: 820 changes made) (rev_ri_upps25: 820 changes made) (rev_ri_upps30: 820 changes made) (rev_ri_upps35: 820 changes made) (rev_ri_upps40: 820 changes made) (rev_ri_upps45: 821 changes made) (rev_ri_upps50: 821 changes made) (rev_ri_upps53: 821 changes made) (rev_ri_upps55: 821 changes made) (rev_ri_upps57: 821 changes made) (rev_ri_upps59: 821 changes made) . . *generates the subscales of the upps-p . . egen uppsmiss = rowmiss(upps1-upps59) . egen negurgtot = rowtotal(rev_ri_upps2 - rev_ri_upps58 ri_upps54) if uppsmiss<30 (1 missing value generated) . alpha rev_ri_upps2-rev_ri_upps58 ri_upps54 Test scale = mean(unstandardized items) Average interitem covariance: .3117926 Number of items in the scale: 12 Scale reliability coefficient: 0.8835 . . egen premedtot = rowtotal(ri_upps1 ri_upps6 ri_upps11 ri_upps16 ri_upps21 /// > ri_upps28 ri_upps33 ri_upps38 ri_upps43 ri_upps49 ) if uppsmiss<30 (1 missing value generated) . alpha ri_upps1 ri_upps6 ri_upps11 ri_upps16 ri_upps21 /// > ri_upps28 ri_upps33 ri_upps38 ri_upps43 ri_upps49 Test scale = mean(unstandardized items) Average interitem covariance: .2188078 Number of items in the scale: 10 Scale reliability coefficient: 0.8487 . . egen persevtot = rowtotal(ri_upps4 rev_ri_upps9 ri_upps14 ri_upps19 ri_upps24 /// > ri_upps27 ri_upps32 ri_upps37 ri_upps42 rev_ri_upps47) if uppsmiss<30 (1 missing value generated) . alpha ri_upps4 rev_ri_upps9 ri_upps14 ri_upps19 ri_upps24 /// > ri_upps27 ri_upps32 ri_upps37 ri_upps42 rev_ri_upps47 Test scale = mean(unstandardized items) Average interitem covariance: .179084 Number of items in the scale: 10 Scale reliability coefficient: 0.8091 . . egen senseektot = rowtotal( rev_ri_upps3 - rev_ri_upps56) if uppsmiss<30 (1 missing value generated) . alpha rev_ri_upps3 - rev_ri_upps56 Test scale = mean(unstandardized items) Average interitem covariance: .3232529 Number of items in the scale: 12 Scale reliability coefficient: 0.8582 . . egen posurgtot = rowtotal( rev_ri_upps5 - rev_ri_upps59) if uppsmiss<30 (1 missing value generated) . alpha rev_ri_upps5 - rev_ri_upps59 Test scale = mean(unstandardized items) Average interitem covariance: .3259336 Number of items in the scale: 14 Scale reliability coefficient: 0.9340 . . *creates standardized variables for urgency and effortful control . *the effortful control variable was then reversed so that higher values . *indicated better effortful control. this uses the full data set so that . *we could compare participants who dropped out of the survey to those . *who finished. . . alpha negurgtot posurgtot if missing~=1, g(urgency) std Test scale = mean(standardized items) Average interitem correlation: 0.6996 Number of items in the scale: 2 Scale reliability coefficient: 0.8233 . alpha persevtot premedtot if missing~=1, g(rev_effortful) std Test scale = mean(standardized items) Average interitem correlation: 0.5191 Number of items in the scale: 2 Scale reliability coefficient: 0.6834 . . gen effortful = rev_effortful*-1 (1 missing value generated) . . *computes the marijuana use variables . . egen mjint = rowtotal(mjuse1 - mjuse28) . gen mjlog = 0 if mjint==0 & missing~=1 (171 missing values generated) . replace mjlog = 1 if mjint>0 & mjint~=. & missing~=1 (171 real changes made) . gen mjcnt = mjint if mjlog == 1 (650 missing values generated) . . . *1 person reported the max mj use (28 times/week) . *this was reduced to 1 value greater than the next . *highest value. . . replace mjint = 25 if mjint>25 & mjint~=. (1 real change made) . replace mjcnt = 25 if mjcnt>25 & mjint~=. (651 real changes made) . . *The mjuse variable was highly skewed so a log . *transformation was performed on this variable . *for use only in the mj problems analysis . *(i.e., not the mj use analyses which used count data) . . gen logmj = ln(mjint) (650 missing values generated) . . *computes MJ problems variable . . egen MACQ = rowtotal(macq1-macq50) if missing~=1 . gen MACQlog = 0 if MACQ==0 & missing~=1 (235 missing values generated) . replace MACQlog = 1 if MACQ~=0 & MACQ~=. & missing~=1 (235 real changes made) . gen MACQcnt = MACQ if MACQlog==1 (586 missing values generated) . . *1 person reported experiencing all 50 mj use problems . *this persons value was reduced 1 greater than the next higher value . . replace MACQcnt = 37 if MACQcnt>37 & MACQcnt~=. (1 real change made) . replace MACQ = 37 if MACQ>37 & MACQ~=. (1 real change made) . . *centers sensation seeking variable . . mcenter senseektot urgency Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- C_senseektot | 820 -5.36e-08 7.364032 -21.91829 14.08171 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- C_urgency | 820 8.04e-09 .9218535 -1.536378 3.25946 . . drop C_urgency . . *Computes interactions for the full use model . . gen URGxDTS = urgency*DTS (1 missing value generated) . gen URGxALS = urgency*ALS (1 missing value generated) . . gen EFFxDTS = effortful*DTS (1 missing value generated) . gen EFFxALS = effortful*ALS (1 missing value generated) . . gen SSxDTS = C_senseektot*DTS (1 missing value generated) . gen SSxALS = C_senseektot*ALS (1 missing value generated) . . *Generates the self-regulation indicators for . *the use count model . . alpha posurg negurg if mjlog==1 & missing~=1, g(urgency1) std Test scale = mean(standardized items) Average interitem correlation: 0.6604 Number of items in the scale: 2 Scale reliability coefficient: 0.7955 . alpha persevtot premedtot if mjlog==1 & missing~=1, g(rev_effortful1) std Test scale = mean(standardized items) Average interitem correlation: 0.4097 Number of items in the scale: 2 Scale reliability coefficient: 0.5812 . . gen effortful1 = rev_effortful1*-1 (650 missing values generated) . . mcenter senseektot urgency1 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- C_senseekt~1 | 171 -8.44e-08 6.485967 -15.63743 11.36257 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- C_urgency1 | 171 -1.70e-09 .9111643 -1.761105 2.994168 . . drop C_urgency1 . . alpha dtstol dtsapp dtsabs dtsreg if missing~=1 & mjlog==1, g(DTS1) std Test scale = mean(standardized items) Average interitem correlation: 0.6528 Number of items in the scale: 4 Scale reliability coefficient: 0.8826 . . alpha alsang alsad alsde if missing~=1 & mjlog==1, g(ALS1) std Test scale = mean(standardized items) Average interitem correlation: 0.5867 Number of items in the scale: 3 Scale reliability coefficient: 0.8099 . . *Computes interactions for the mj users only . . gen URGxDTS1 = urgency1*DTS1 (650 missing values generated) . gen URGxALS1 = urgency1*ALS1 (650 missing values generated) . . gen EFFxDTS1 = effortful1*DTS1 (650 missing values generated) . gen EFFxALS1 = effortful1*ALS1 (650 missing values generated) . . gen SSxDTS1 = C_senseektot1*DTS1 (650 missing values generated) . gen SSxALS1 = C_senseektot1*ALS1 (650 missing values generated) . . *Generates the self-regulation indicators for . *those who use mj and reported some problems . . alpha posurg negurg if mjlog==1 & missing~=1 & MACQlog==1, g(urgency2) std Test scale = mean(standardized items) Average interitem correlation: 0.6725 Number of items in the scale: 2 Scale reliability coefficient: 0.8042 . alpha persevtot premedtot if mjlog==1 & missing~=1 & MACQlog==1, g(rev_effortful2) std Test scale = mean(standardized items) Average interitem correlation: 0.4234 Number of items in the scale: 2 Scale reliability coefficient: 0.5949 . . gen effortful2 = rev_effortful2*-1 (672 missing values generated) . . mcenter senseektot urgency2 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- C_senseekt~2 | 149 -9.04e-08 6.499376 -15.69128 11.30872 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- C_urgency2 | 149 -3.36e-09 .9144603 -1.815198 2.900115 . . drop C_urgency2 . . alpha dtstol dtsapp dtsabs dtsreg if missing~=1 & mjlog==1 & MACQlog==1, g(DTS2) std Test scale = mean(standardized items) Average interitem correlation: 0.6483 Number of items in the scale: 4 Scale reliability coefficient: 0.8806 . . alpha alsang alsad alsde if missing~=1 & mjlog==1 & MACQlog==1, g(ALS2) std Test scale = mean(standardized items) Average interitem correlation: 0.5796 Number of items in the scale: 3 Scale reliability coefficient: 0.8053 . . *Computes interactions for the mj users w/ >0 problems . . gen URGxDTS2 = urgency2*DTS2 (672 missing values generated) . gen URGxALS2 = urgency2*ALS2 (672 missing values generated) . . gen EFFxDTS2 = effortful2*DTS2 (672 missing values generated) . gen EFFxALS2 = effortful2*ALS2 (672 missing values generated) . . gen SSxDTS2 = C_senseektot2*DTS2 (672 missing values generated) . gen SSxALS2 = C_senseektot2*ALS2 (672 missing values generated) . . . logistic mjlog sex age negaff posaff urgency /// > effortful C_senseektot DTS ALS /// > , vce(robust) Logistic regression Number of obs = 819 Wald chi2(9) = 61.10 Prob > chi2 = 0.0000 Log pseudolikelihood = -386.0165 Pseudo R2 = 0.0801 ------------------------------------------------------------------------------ | Robust mjlog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | 1.630966 .3049783 2.62 0.009 1.130516 2.352953 age | .9581019 .0398959 -1.03 0.304 .8830132 1.039576 negaff | 1.237515 .2004475 1.32 0.188 .9009 1.699905 posaff | .9316612 .1332233 -0.50 0.621 .7039472 1.233037 urgency | 1.175457 .156096 1.22 0.223 .9060886 1.524906 effortful | .7519909 .0859619 -2.49 0.013 .6010487 .9408393 C_senseektot | 1.062916 .0150503 4.31 0.000 1.033824 1.092827 DTS | .8330207 .1099356 -1.38 0.166 .6431626 1.078924 ALS | .8087869 .1065453 -1.61 0.107 .6247429 1.047049 _cons | .3788651 .394265 -0.93 0.351 .0492815 2.91263 ------------------------------------------------------------------------------ . . ztnb mjcnt sex age negaff posaff urgency1 /// > effortful1 C_senseektot1 DTS1 ALS1 /// > , vce(robust) irr Fitting Zero-truncated poisson model: Iteration 0: log pseudolikelihood = -533.66274 Iteration 1: log pseudolikelihood = -533.14697 Iteration 2: log pseudolikelihood = -533.14633 Iteration 3: log pseudolikelihood = -533.14633 Fitting constant-only model: Iteration 0: log pseudolikelihood = -402.07721 Iteration 1: log pseudolikelihood = -389.67663 Iteration 2: log pseudolikelihood = -389.40253 Iteration 3: log pseudolikelihood = -389.40222 Iteration 4: log pseudolikelihood = -389.40221 Fitting full model: Iteration 0: log pseudolikelihood = -380.90646 Iteration 1: log pseudolikelihood = -377.6874 Iteration 2: log pseudolikelihood = -377.217 Iteration 3: log pseudolikelihood = -377.2077 Iteration 4: log pseudolikelihood = -377.20768 Zero-truncated negative binomial regression Number of obs = 171 Dispersion = mean Wald chi2(9) = 36.25 Log likelihood = -377.20768 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------- | Robust mjcnt | IRR Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- sex | 2.479834 .6013624 3.75 0.000 1.541719 3.988778 age | 1.118399 .0562247 2.23 0.026 1.013456 1.234209 negaff | 1.167439 .2285187 0.79 0.429 .7954571 1.713371 posaff | 1.004845 .1761381 0.03 0.978 .7126778 1.416789 urgency1 | 1.274368 .1858476 1.66 0.096 .9575461 1.696016 effortful1 | .8484877 .1131342 -1.23 0.218 .6533555 1.101898 C_senseektot1 | .9985486 .0175289 -0.08 0.934 .9647768 1.033503 DTS1 | .9011431 .136931 -0.69 0.493 .6690391 1.213769 ALS1 | .9271589 .1518698 -0.46 0.644 .6725526 1.278151 _cons | .1060738 .1196662 -1.99 0.047 .0116234 .9680175 --------------+---------------------------------------------------------------- /lnalpha | .762766 .3519371 .072982 1.45255 --------------+---------------------------------------------------------------- alpha | 2.144199 .7546231 1.075711 4.273999 ------------------------------------------------------------------------------- . . logistic MACQlog sex age logmj negaff posaff /// > urgency1 effortful1 C_senseektot1 DTS1 ALS1 /// > , vce(robust) Logistic regression Number of obs = 171 Wald chi2(10) = 28.16 Prob > chi2 = 0.0017 Log pseudolikelihood = -50.504099 Pseudo R2 = 0.2305 ------------------------------------------------------------------------------- | Robust MACQlog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- sex | .4506977 .2981795 -1.20 0.228 .1232351 1.6483 age | .8119611 .0695086 -2.43 0.015 .6865425 .9602914 logmj | 3.615994 1.499875 3.10 0.002 1.603831 8.152615 negaff | .4365298 .3019425 -1.20 0.231 .1125247 1.693479 posaff | 1.351395 .5828054 0.70 0.485 .5803521 3.14683 urgency1 | 2.263331 .8093517 2.28 0.022 1.12296 4.561755 effortful1 | .9069721 .294292 -0.30 0.763 .4801736 1.713127 C_senseektot1 | .988375 .0476958 -0.24 0.809 .8991776 1.086421 DTS1 | .389394 .187365 -1.96 0.050 .1516408 .9999135 ALS1 | .8261809 .2328708 -0.68 0.498 .4755015 1.435484 _cons | 744.3724 2297.953 2.14 0.032 1.754061 315890 ------------------------------------------------------------------------------- . . ztnb MACQcnt sex age logmj negaff posaff urgency2 /// > effortful2 C_senseektot2 DTS2 ALS2 /// > , vce(robust) irr Fitting Zero-truncated poisson model: Iteration 0: log pseudolikelihood = -523.96693 Iteration 1: log pseudolikelihood = -522.52419 Iteration 2: log pseudolikelihood = -522.52218 Iteration 3: log pseudolikelihood = -522.52218 Fitting constant-only model: Iteration 0: log pseudolikelihood = -445.14557 Iteration 1: log pseudolikelihood = -443.84681 Iteration 2: log pseudolikelihood = -443.80692 Iteration 3: log pseudolikelihood = -443.80684 Iteration 4: log pseudolikelihood = -443.80684 Fitting full model: Iteration 0: log pseudolikelihood = -422.21547 Iteration 1: log pseudolikelihood = -411.137 Iteration 2: log pseudolikelihood = -410.70948 Iteration 3: log pseudolikelihood = -410.70531 Iteration 4: log pseudolikelihood = -410.70531 Zero-truncated negative binomial regression Number of obs = 149 Dispersion = mean Wald chi2(10) = 93.96 Log likelihood = -410.70531 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------- | Robust MACQcnt | IRR Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- sex | 1.226201 .1784394 1.40 0.161 .9219195 1.630911 age | .984083 .0209964 -0.75 0.452 .9437793 1.026108 logmj | 1.707739 .1352179 6.76 0.000 1.462258 1.994432 negaff | 1.061754 .1210531 0.53 0.599 .8491347 1.327613 posaff | .9199107 .1149148 -0.67 0.504 .7201351 1.175107 urgency2 | 1.23753 .1195585 2.21 0.027 1.024049 1.495515 effortful2 | 1.054142 .0873713 0.64 0.525 .8960835 1.240081 C_senseektot2 | 1.016197 .0124869 1.31 0.191 .992016 1.040968 DTS2 | .9453026 .1084177 -0.49 0.624 .7549978 1.183576 ALS2 | 1.066819 .1053488 0.66 0.512 .8790924 1.294635 _cons | 4.969299 3.157872 2.52 0.012 1.430134 17.26687 --------------+---------------------------------------------------------------- /lnalpha | -.7430529 .2195971 -1.173455 -.3126504 --------------+---------------------------------------------------------------- alpha | .4756596 .1044535 .3092964 .7315056 ------------------------------------------------------------------------------- . . **************************************************************** . logistic mjlog sex age negaff posaff urgency /// > effortful C_senseektot DTS ALS /// > , vce(robust) Logistic regression Number of obs = 819 Wald chi2(9) = 61.10 Prob > chi2 = 0.0000 Log pseudolikelihood = -386.0165 Pseudo R2 = 0.0801 ------------------------------------------------------------------------------ | Robust mjlog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | 1.630966 .3049783 2.62 0.009 1.130516 2.352953 age | .9581019 .0398959 -1.03 0.304 .8830132 1.039576 negaff | 1.237515 .2004475 1.32 0.188 .9009 1.699905 posaff | .9316612 .1332233 -0.50 0.621 .7039472 1.233037 urgency | 1.175457 .156096 1.22 0.223 .9060886 1.524906 effortful | .7519909 .0859619 -2.49 0.013 .6010487 .9408393 C_senseektot | 1.062916 .0150503 4.31 0.000 1.033824 1.092827 DTS | .8330207 .1099356 -1.38 0.166 .6431626 1.078924 ALS | .8087869 .1065453 -1.61 0.107 .6247429 1.047049 _cons | .3788651 .394265 -0.93 0.351 .0492815 2.91263 ------------------------------------------------------------------------------ . . ztnb mjcnt sex age negaff posaff urgency /// > effortful C_senseektot DTS ALS /// > if mjcnt>0, vce(robust) irr Fitting Zero-truncated poisson model: Iteration 0: log pseudolikelihood = -4065.2336 Iteration 1: log pseudolikelihood = -4065.2336 Fitting constant-only model: Iteration 0: log pseudolikelihood = -3280.4093 Iteration 1: log pseudolikelihood = -3175.8781 Iteration 2: log pseudolikelihood = -3171.005 Iteration 3: log pseudolikelihood = -3171.0018 Iteration 4: log pseudolikelihood = -3171.0018 Fitting full model: Iteration 0: log pseudolikelihood = -3159.0455 Iteration 1: log pseudolikelihood = -3158.7168 Iteration 2: log pseudolikelihood = -3158.7166 Zero-truncated negative binomial regression Number of obs = 819 Dispersion = mean Wald chi2(9) = 45.72 Log likelihood = -3158.7166 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust mjcnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | .9419716 .0324691 -1.73 0.083 .8804353 1.007809 age | 1.010506 .0071434 1.48 0.139 .9966022 1.024605 negaff | .9606018 .02854 -1.35 0.176 .9062619 1.0182 posaff | 1.007915 .0255379 0.31 0.756 .9590842 1.059232 urgency | .9827248 .0223127 -0.77 0.443 .9399514 1.027445 effortful | 1.044927 .0201539 2.28 0.023 1.006163 1.085184 C_senseektot | .9909109 .0021873 -4.14 0.000 .9866332 .9952071 DTS | 1.025014 .0232837 1.09 0.277 .98038 1.071681 ALS | 1.030526 .0190603 1.63 0.104 .9938377 1.068569 _cons | 17.86731 3.340539 15.42 0.000 12.38555 25.77526 -------------+---------------------------------------------------------------- /lnalpha | -1.102346 .115618 -1.328953 -.8757393 -------------+---------------------------------------------------------------- alpha | .332091 .0383957 .2647542 .4165539 ------------------------------------------------------------------------------ . . logistic MACQlog sex age logmj negaff posaff /// > urgency effortful C_senseektot DTS ALS /// > , vce(robust) Logistic regression Number of obs = 171 Wald chi2(10) = 28.15 Prob > chi2 = 0.0017 Log pseudolikelihood = -50.472013 Pseudo R2 = 0.2310 ------------------------------------------------------------------------------ | Robust MACQlog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | .4520989 .2991356 -1.20 0.230 .1236027 1.653633 age | .8113324 .0698005 -2.43 0.015 .6854382 .9603494 logmj | 3.614239 1.498445 3.10 0.002 1.603663 8.145556 negaff | .4297235 .3003107 -1.21 0.227 .109227 1.690628 posaff | 1.36064 .5896443 0.71 0.477 .5819268 3.1814 urgency | 2.308862 .8454445 2.29 0.022 1.126446 4.732445 effortful | .9002588 .2987794 -0.32 0.752 .4697526 1.725304 C_senseektot | .9882672 .0478262 -0.24 0.807 .8988377 1.086594 DTS | .3958994 .1861492 -1.97 0.049 .1575272 .9949792 ALS | .8210059 .239131 -0.68 0.498 .4638956 1.453022 _cons | 507.8361 1511.375 2.09 0.036 1.487494 173377.1 ------------------------------------------------------------------------------ . . ztnb MACQcnt sex age logmj negaff posaff urgency /// > effortful C_senseektot DTS ALS /// > , vce(robust) irr Fitting Zero-truncated poisson model: Iteration 0: log pseudolikelihood = -524.31 Iteration 1: log pseudolikelihood = -522.79985 Iteration 2: log pseudolikelihood = -522.79796 Iteration 3: log pseudolikelihood = -522.79796 Fitting constant-only model: Iteration 0: log pseudolikelihood = -445.14557 Iteration 1: log pseudolikelihood = -443.84681 Iteration 2: log pseudolikelihood = -443.80692 Iteration 3: log pseudolikelihood = -443.80684 Iteration 4: log pseudolikelihood = -443.80684 Fitting full model: Iteration 0: log pseudolikelihood = -422.26106 Iteration 1: log pseudolikelihood = -411.21633 Iteration 2: log pseudolikelihood = -410.79769 Iteration 3: log pseudolikelihood = -410.79362 Iteration 4: log pseudolikelihood = -410.79362 Zero-truncated negative binomial regression Number of obs = 149 Dispersion = mean Wald chi2(10) = 93.28 Log likelihood = -410.79362 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust MACQcnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | 1.219242 .1775899 1.36 0.174 .9164468 1.622081 age | .9840581 .0209717 -0.75 0.451 .9438009 1.026032 logmj | 1.707918 .1353466 6.75 0.000 1.462219 1.994903 negaff | 1.06772 .1224264 0.57 0.568 .8528196 1.336772 posaff | .9181357 .1147775 -0.68 0.494 .718616 1.173051 urgency | 1.242613 .1219713 2.21 0.027 1.025143 1.506216 effortful | 1.059401 .0888096 0.69 0.491 .8988855 1.248581 C_senseektot | 1.016258 .0124909 1.31 0.189 .9920691 1.041037 DTS | .9494903 .1047821 -0.47 0.639 .764813 1.178761 ALS | 1.059083 .1085679 0.56 0.575 .8663079 1.294755 _cons | 4.35745 2.797722 2.29 0.022 1.237982 15.33736 -------------+---------------------------------------------------------------- /lnalpha | -.7413157 .219177 -1.170895 -.3117367 -------------+---------------------------------------------------------------- alpha | .4764866 .1044349 .3100894 .7321743 ------------------------------------------------------------------------------ . . logistic mjlog sex age negaff posaff urgency /// > effortful C_senseektot DTS ALS Logistic regression Number of obs = 819 LR chi2(9) = 67.20 Prob > chi2 = 0.0000 Log likelihood = -386.0165 Pseudo R2 = 0.0801 ------------------------------------------------------------------------------ mjlog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | 1.630966 .3054947 2.61 0.009 1.129815 2.354413 age | .9581019 .0376083 -1.09 0.276 .8871552 1.034722 negaff | 1.237515 .2006085 1.31 0.189 .9006704 1.700338 posaff | .9316612 .1307516 -0.50 0.614 .7076171 1.226642 urgency | 1.175457 .1542625 1.23 0.218 .9088628 1.520251 effortful | .7519909 .0884914 -2.42 0.015 .5970991 .9470628 C_senseektot | 1.062916 .0153066 4.24 0.000 1.033335 1.093344 DTS | .8330207 .1072892 -1.42 0.156 .6471798 1.072227 ALS | .8087869 .1069125 -1.61 0.108 .6241872 1.047981 _cons | .3788651 .3663174 -1.00 0.315 .0569474 2.520549 ------------------------------------------------------------------------------ . . ztnb mjcnt sex age negaff posaff urgency /// > effortful C_senseektot DTS ALS /// > if mjcnt>0, irr Fitting Zero-truncated poisson model: Iteration 0: log likelihood = -4065.2336 Iteration 1: log likelihood = -4065.2336 Fitting constant-only model: Iteration 0: log likelihood = -3280.4093 Iteration 1: log likelihood = -3175.8781 Iteration 2: log likelihood = -3171.005 Iteration 3: log likelihood = -3171.0018 Iteration 4: log likelihood = -3171.0018 Fitting full model: Iteration 0: log likelihood = -3159.0455 Iteration 1: log likelihood = -3158.7168 Iteration 2: log likelihood = -3158.7166 Zero-truncated negative binomial regression Number of obs = 819 LR chi2(9) = 24.57 Dispersion = mean Prob > chi2 = 0.0035 Log likelihood = -3158.7166 Pseudo R2 = 0.0039 ------------------------------------------------------------------------------ mjcnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | .9419716 .0443487 -1.27 0.204 .8589395 1.03303 age | 1.010506 .0094274 1.12 0.263 .9921969 1.029154 negaff | .9606018 .0386901 -1.00 0.318 .8876865 1.039506 posaff | 1.007915 .0343899 0.23 0.817 .9427164 1.077623 urgency | .9827248 .0315401 -0.54 0.587 .9228115 1.046528 effortful | 1.044927 .0292486 1.57 0.116 .989145 1.103855 C_senseektot | .9909109 .0032392 -2.79 0.005 .9845825 .9972799 DTS | 1.025014 .0319582 0.79 0.428 .9642526 1.089605 ALS | 1.030526 .0299277 1.04 0.300 .9735071 1.090885 _cons | 17.86731 4.211214 12.23 0.000 11.25735 28.35843 -------------+---------------------------------------------------------------- /lnalpha | -1.102346 .0658764 -1.231462 -.973231 -------------+---------------------------------------------------------------- alpha | .332091 .021877 .2918656 .3778602 ------------------------------------------------------------------------------ Likelihood-ratio test of alpha=0: chibar2(01) = 1813.03 Prob>=chibar2 = 0.000 . . logistic MACQlog sex age logmj negaff posaff /// > urgency effortful C_senseektot DTS ALS Logistic regression Number of obs = 171 LR chi2(10) = 30.32 Prob > chi2 = 0.0008 Log likelihood = -50.472013 Pseudo R2 = 0.2310 ------------------------------------------------------------------------------ MACQlog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | .4520989 .2561714 -1.40 0.161 .1489086 1.37261 age | .8113324 .0826258 -2.05 0.040 .6645271 .9905694 logmj | 3.614239 1.516438 3.06 0.002 1.588092 8.225422 negaff | .4297235 .239891 -1.51 0.130 .1438832 1.283418 posaff | 1.36064 .5678184 0.74 0.461 .6005129 3.082934 urgency | 2.308862 .9764705 1.98 0.048 1.007872 5.289207 effortful | .9002588 .3327088 -0.28 0.776 .4363035 1.857574 C_senseektot | .9882672 .0447405 -0.26 0.794 .9043552 1.079965 DTS | .3958994 .1744137 -2.10 0.035 .1669504 .9388194 ALS | .8210059 .3020571 -0.54 0.592 .3991902 1.688545 _cons | 507.8361 1402.635 2.26 0.024 2.263176 113953.8 ------------------------------------------------------------------------------ . . ztnb MACQcnt sex age logmj negaff posaff urgency /// > effortful C_senseektot DTS ALS /// > , irr Fitting Zero-truncated poisson model: Iteration 0: log likelihood = -524.31 Iteration 1: log likelihood = -522.79985 Iteration 2: log likelihood = -522.79796 Iteration 3: log likelihood = -522.79796 Fitting constant-only model: Iteration 0: log likelihood = -445.14557 Iteration 1: log likelihood = -443.84681 Iteration 2: log likelihood = -443.80692 Iteration 3: log likelihood = -443.80684 Iteration 4: log likelihood = -443.80684 Fitting full model: Iteration 0: log likelihood = -422.26106 Iteration 1: log likelihood = -411.21633 Iteration 2: log likelihood = -410.79769 Iteration 3: log likelihood = -410.79362 Iteration 4: log likelihood = -410.79362 Zero-truncated negative binomial regression Number of obs = 149 LR chi2(10) = 66.03 Dispersion = mean Prob > chi2 = 0.0000 Log likelihood = -410.79362 Pseudo R2 = 0.0744 ------------------------------------------------------------------------------ MACQcnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- sex | 1.219242 .1911609 1.26 0.206 .8966703 1.657857 age | .9840581 .0292374 -0.54 0.589 .9283904 1.043064 logmj | 1.707918 .1414519 6.46 0.000 1.45201 2.00893 negaff | 1.06772 .1355611 0.52 0.606 .8325034 1.369394 posaff | .9181357 .1089913 -0.72 0.472 .7275474 1.158651 urgency | 1.242613 .1268315 2.13 0.033 1.017314 1.517807 effortful | 1.059401 .1020259 0.60 0.549 .8771733 1.279487 C_senseektot | 1.016258 .0119208 1.37 0.169 .9931604 1.039893 DTS | .9494903 .1008843 -0.49 0.626 .7709915 1.169315 ALS | 1.059083 .1139203 0.53 0.594 .8577692 1.307644 _cons | 4.35745 3.169159 2.02 0.043 1.047506 18.12627 -------------+---------------------------------------------------------------- /lnalpha | -.7413157 .2052637 -1.143625 -.3390062 -------------+---------------------------------------------------------------- alpha | .4764866 .0978054 .3186617 .712478 ------------------------------------------------------------------------------ Likelihood-ratio test of alpha=0: chibar2(01) = 224.01 Prob>=chibar2 = 0.000