FCM Analysis with interpretation.doc
Transcript of FCM Analysis with interpretation.doc
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7/29/2019 FCM Analysis with interpretation.doc
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Logistic Regression
Dependent Variable Encoding
Original Value Internal Value
No 0
Yes 1
Categorical Variables Codings
Frequency
Parametercoding
(1)
Employment unemployed 37 1.000
employed 4 .000
Gender Male 4 1.000
Female 37 .000
Block 0: Beginning Block
Classification Table(a,b)
Observed Predicted
Practice (Is Px
Compliant)Percentage
CorrectNo Yes
Step 0 Practice (Is PxCompliant)
No0 14 .0
Yes 0 27 100.0
Overall Percentage 65.9
a Constant is included in the model.b The cut value is .500
Block 1: Method = Enter
Model Summary
Step-2 Log
likelihoodCox & SnellR Square
Nagelkerke RSquare
1 40.385 .258 .357
Hosmer and Lemeshow Test
Step Chi-square df Sig.1 5.379 8 .716
Classification Table(a)
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Observed Predicted
Practice (Is Px
Compliant)Percentage
CorrectNo Yes
Step 1 Practice (Is Px
Compliant)
No
6 8 42.9Yes 4 23 85.2
Overall Percentage 70.7
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95.0% C.I.for E
Lower
Step 1(a) AGE -.086 .052 2.760 1 .097 .918 .829
GEN_REC(1)
-.933 1.676 .310 1 .578 .393 .015
EMP_REC(1)
2.588 1.426 3.295 1 .069 13.302 .814
DM_YR .144 .074 3.729 1 .053 1.154 .998
DM_REL -.874 1.088 .645 1 .422 .417 .050
EDUC_ATT
1.019 .694 2.156 1 .142 2.769 .711
KNOWL_SC
-.534 .247 4.671 1 .031 .586 .361
ATT_SC -.015 .031 .239 1 .625 .985 .926
Constant 6.161 4.715 1.708 1 .191 474.013
a Variable(s) entered on step 1: AGE, GEN_REC, EMP_REC, DM_YR, DM_REL, EDUC_ATT, KNOWL_SC,ATT_SC.
Note: Only Knowledge Score is statistically significant in predicting Compliance(the rest are may be due to chance): OR = 0.586, 95% CI: 0.361 0.951, p = 0.031.Interpretation of OR: for every increase in the Knowledge score by 1 point, theresan increase of odds of having compliance by 0.586.
Heres my own re-coding of the variables:
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Variables:
Age
Gender [Gen_Rec] (0 = male, 1 = female)
Employment [Emp_Rec] (I used Employment instead of Occupation)
(0 = unemployed, 1 = employed)
DM_yr = Number of years with DM type 2 (I used DM_yr instead ofDiabnumyears)
DM_rel = Does Px have Relatives with DM type 2? (0 = no, 1 = yes)
(I used DM_rel instead of Diabrel)
Educ_att = Educational attainment (0 = none, 1 = elementary, 2 = high
school, 3 = college)
(I used Educ_att instead of Edattain)
Knowl_Sc = total score gotten in the knowledge test (I used Knowl_Sc instead
of Knowledgeover)
Att_Sc = total score gotten in the attitude test (I used Att_Sc instead of
Attitudeover)