**Moderators:** statman, Analyst Techy, andris, Fierce, GerineL, Smash

I am trying to determine the reason why(and how many) people with health insurance do not fully use all of its benefits(like free flu vaccines). I am using a sample of 400 people with age, income, education as dependent variables and having health insurance as independent variable. I glanced at the information in http://www-01.ibm.com/support/docview.w ... wg21476743 and followed the mentioned steps.

I got some results like

Multivariate Tests (Design: Intercept + haveinsure)

Effect Value F Hypothesis df Error df Sig.

Intercept Pillai's Trace .053 11.361(b) 3.000 470.000 .000

Wilks' Lambda .827 11.361(b) 3.000 470.000 .000

Hotelling's

Trace .069 11.361(b) 3.000 470.000 .000

Roy's Largest

Root .083 11.361(b) 3.000 470.000 .000

haveinsure Pillai's Trace .138 4.570 12.000 1420.000 .000

Wilks' Lambda .877 4.797 12.000 1248.086 .000

Hotelling's

Trace .151 4.998 12.000 1410.000 .000

Roy's Largest

Root .141 16.101(c) 4.000 473.000 .000

b - Exact statistic

c The statistic is an upper bound on F that yields a lower bound on the significance level

Tests of Between-Subjects Effects Tests

Source Dependent

Variable Type III df Mean F Sig.

Sum of Squares Square

Corrected Model age 37.546(a) 4 9.637 3.893 .004

education 10.619(b) 4 2.655 .477 .752

income 334.245(c) 4 84.061 16.766 .000

Intercept age 32.173 1 34.173 13.805 .000

education 141.268 1 143.268 25.752 .000

income 30.201 1 30.201 6.024 .014

haveinsure age 37.546 4 9.637 3.893 .004

education 10.619 4 2.655 .477 .752

income 335.245 4 84.061 16.766 .000

Error age 1171.320 474 2.475

education 2636.013 474 5.563

income 2375.494 474 5.014

Total age 3150.000 479

education 12315.000 479

income 6289.000 479

Corrected Total age 1210.866 478

education 2646.633 478

income 2711.739 478

a. R Squared = .032 (Adjusted R Squared = .024)

b. R Squared = .004 (Adjusted R Squared = -.004)

c. R Squared = .124 (Adjusted R Squared = .117)

Dependent Parameter B Std. t Sig. 95% Confidence Interval

Variable Error Lower Upper

Bound Bound

age Intercept 1 1.573 0.637 0.525 -2.092 4.092

[haveinsure=1] 1.173 1.576 0.745 0.456 -1.923 4.268

[haveinsure=2] 0.589 1.578 0.373 0.708 -2.514 3.693

education Intercept 4 2.358 1.697 0.091 -0.635 8.636

[haveinsure=1] 0.578 2.362 0.245 0.808 -4.063 5.219

[haveinsure=2] 0.388 2.367 0.164 0.87 -4.265 5.04

income Intercept 1 2.238 0.448 0.659 -3.4 5.4

[haveinsure=1] 2.289 2.242 1.021 0.309 -2.118 6.696

[haveinsure=2] 0.419 2.245 0.188 0.852 -3.999 4.837

1. Am I approaching the problem in a proper way? I mean am I doing the right analysis in SPSS for Multivariate linear regression analysis (multiple dependent variable, one independent variable)?

2. Which method(Pillai's Trace, Wilks' Lambda, Hotelling's Trace, Roy's Largest Root) should be used for a case like mine?

3. Why is Type III Sum of Squares error 1171.320 for age, education and income?

4. I am new to Multivariate linear regression analysis and also a beginner with SPSS and statistics. How can I interpret and learn more about the output SPSS generated?

Any suggestions would be appreciated.

Thanks

I got some results like

Multivariate Tests (Design: Intercept + haveinsure)

Effect Value F Hypothesis df Error df Sig.

Intercept Pillai's Trace .053 11.361(b) 3.000 470.000 .000

Wilks' Lambda .827 11.361(b) 3.000 470.000 .000

Hotelling's

Trace .069 11.361(b) 3.000 470.000 .000

Roy's Largest

Root .083 11.361(b) 3.000 470.000 .000

haveinsure Pillai's Trace .138 4.570 12.000 1420.000 .000

Wilks' Lambda .877 4.797 12.000 1248.086 .000

Hotelling's

Trace .151 4.998 12.000 1410.000 .000

Roy's Largest

Root .141 16.101(c) 4.000 473.000 .000

b - Exact statistic

c The statistic is an upper bound on F that yields a lower bound on the significance level

Tests of Between-Subjects Effects Tests

Source Dependent

Variable Type III df Mean F Sig.

Sum of Squares Square

Corrected Model age 37.546(a) 4 9.637 3.893 .004

education 10.619(b) 4 2.655 .477 .752

income 334.245(c) 4 84.061 16.766 .000

Intercept age 32.173 1 34.173 13.805 .000

education 141.268 1 143.268 25.752 .000

income 30.201 1 30.201 6.024 .014

haveinsure age 37.546 4 9.637 3.893 .004

education 10.619 4 2.655 .477 .752

income 335.245 4 84.061 16.766 .000

Error age 1171.320 474 2.475

education 2636.013 474 5.563

income 2375.494 474 5.014

Total age 3150.000 479

education 12315.000 479

income 6289.000 479

Corrected Total age 1210.866 478

education 2646.633 478

income 2711.739 478

a. R Squared = .032 (Adjusted R Squared = .024)

b. R Squared = .004 (Adjusted R Squared = -.004)

c. R Squared = .124 (Adjusted R Squared = .117)

Dependent Parameter B Std. t Sig. 95% Confidence Interval

Variable Error Lower Upper

Bound Bound

age Intercept 1 1.573 0.637 0.525 -2.092 4.092

[haveinsure=1] 1.173 1.576 0.745 0.456 -1.923 4.268

[haveinsure=2] 0.589 1.578 0.373 0.708 -2.514 3.693

education Intercept 4 2.358 1.697 0.091 -0.635 8.636

[haveinsure=1] 0.578 2.362 0.245 0.808 -4.063 5.219

[haveinsure=2] 0.388 2.367 0.164 0.87 -4.265 5.04

income Intercept 1 2.238 0.448 0.659 -3.4 5.4

[haveinsure=1] 2.289 2.242 1.021 0.309 -2.118 6.696

[haveinsure=2] 0.419 2.245 0.188 0.852 -3.999 4.837

1. Am I approaching the problem in a proper way? I mean am I doing the right analysis in SPSS for Multivariate linear regression analysis (multiple dependent variable, one independent variable)?

2. Which method(Pillai's Trace, Wilks' Lambda, Hotelling's Trace, Roy's Largest Root) should be used for a case like mine?

3. Why is Type III Sum of Squares error 1171.320 for age, education and income?

4. I am new to Multivariate linear regression analysis and also a beginner with SPSS and statistics. How can I interpret and learn more about the output SPSS generated?

Any suggestions would be appreciated.

Thanks

please provide the syntax you used, and upload a file with your results, because they are quite hard to read now

In SPSS22, I chose Analyze->Regression->Binary Logistic, then choose dependent variable as people who free insurance services(like preventive services), independent variable as age, education, income, Method as Enter. I did not choose anything in Save, Options, Bootstrap, Style.

LOGISTIC REGRESSION VARIABLES Notusing_free_insurance_services

/METHOD=ENTER Age Gender Education Income

/CONTRAST (Age)=Indicator

/CONTRAST (Gender)=Indicator

/CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

The results are jumbled up since I copied from the spv file(SPSS output).

I got

Block 0: Beginning Block

Classification Table(a,b)

|------|----------------------------------|-----------------------------------------------------|

| |Observed |Predicted |

| | |----------------------------------|------------------|

| | |Notusing_free_insurance_services |Percentage Correct|

| | |------------------------------|---| |

| | |.0 |1.0| |

|------|------------------------------|---|------------------------------|---|------------------|

|Step 0|Notusing_free_insurance_services|.0 |293 |0 |100.0 |

| | |---|------------------------------|---|------------------|

| | |1.0|48 |0 |.0 |

| |----------------------------------|------------------------------|---|------------------|

| |Overall Percentage | | |85.9 |

|-----------------------------------------------------------------------------------------------|

a Constant is included in the model.

b The cut value is .500

1. I can understand this, but need to know what is cut value.

Variables in the Equation

|---------------|------|----|-------|--|----|------|

| |B |S.E.|Wald |df|Sig.|Exp(B)|

|------|--------|------|----|-------|--|----|------|

|Step 0|Constant|-1.809|.156|134.964|1 |.000|.164 |

|--------------------------------------------------|

2. I need to read up on B, S.E.,Wald, df, Sig, Exp(B), but what should I interpret from these?

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

|------------|----------|--|----|

| |Chi-square|df|Sig.|

|------|-----|----------|--|----|

|Step 1|Step |37.079 |10|.000|

| |-----|----------|--|----|

| |Block|37.079 |10|.000|

| |-----|----------|--|----|

| |Model|37.079 |10|.000|

|-------------------------------|

Model Summary

|----|-----------------|--------------------|-------------------|

|Step|-2 Log likelihood|Cox & Snell R Square|Nagelkerke R Square|

|----|-----------------|--------------------|-------------------|

|1 |240.048a |.103 |.185 |

|---------------------------------------------------------------|

a Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.

3. Does that mean I need to ignore -2 Log likelihood method's results?

Classification Tablea

|------|----------------------------------|-----------------------------------------------------|

| |Observed |Predicted |

| | |----------------------------------|------------------|

| | |Notusing_free_insurance_services |Percentage Correct|

| | |------------------------------|---| |

| | |.0 |1.0| |

|------|------------------------------|---|------------------------------|---|------------------|

|Step 1|Notusing_free_insurance_services|.0 |293 |0 |100.0 |

| | |---|------------------------------|---|------------------|

| | |1.0|47 |1 |2.1 |

| |----------------------------------|------------------------------|---|------------------|

| |Overall Percentage | | |86.2 |

|-----------------------------------------------------------------------------------------------|

a The cut value is .500

Variables in the Equation

|---------------|------|----|-------|--|----|------|

| |B |S.E.|Wald |df|Sig.|Exp(B)|

|------|--------|------|----|-------|--|----|------|

|Step 0|Constant|-1.809|.156|134.964|1 |.000|.164 |

|--------------------------------------------------|

4. I should have taken some statistics classes so that I can understand the output, but does the output indicate what I estimated earlier(People with relatively low income, education and those who are younger(18 to 24) are the group who use free insurance services less compared to other groups)?

I tried to attach a text file with this output, but got the message "Sorry, the board attachment quota has been reached."

Thank you for helping me out.

LOGISTIC REGRESSION VARIABLES Notusing_free_insurance_services

/METHOD=ENTER Age Gender Education Income

/CONTRAST (Age)=Indicator

/CONTRAST (Gender)=Indicator

/CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

The results are jumbled up since I copied from the spv file(SPSS output).

I got

Block 0: Beginning Block

Classification Table(a,b)

|------|----------------------------------|-----------------------------------------------------|

| |Observed |Predicted |

| | |----------------------------------|------------------|

| | |Notusing_free_insurance_services |Percentage Correct|

| | |------------------------------|---| |

| | |.0 |1.0| |

|------|------------------------------|---|------------------------------|---|------------------|

|Step 0|Notusing_free_insurance_services|.0 |293 |0 |100.0 |

| | |---|------------------------------|---|------------------|

| | |1.0|48 |0 |.0 |

| |----------------------------------|------------------------------|---|------------------|

| |Overall Percentage | | |85.9 |

|-----------------------------------------------------------------------------------------------|

a Constant is included in the model.

b The cut value is .500

1. I can understand this, but need to know what is cut value.

Variables in the Equation

|---------------|------|----|-------|--|----|------|

| |B |S.E.|Wald |df|Sig.|Exp(B)|

|------|--------|------|----|-------|--|----|------|

|Step 0|Constant|-1.809|.156|134.964|1 |.000|.164 |

|--------------------------------------------------|

2. I need to read up on B, S.E.,Wald, df, Sig, Exp(B), but what should I interpret from these?

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

|------------|----------|--|----|

| |Chi-square|df|Sig.|

|------|-----|----------|--|----|

|Step 1|Step |37.079 |10|.000|

| |-----|----------|--|----|

| |Block|37.079 |10|.000|

| |-----|----------|--|----|

| |Model|37.079 |10|.000|

|-------------------------------|

Model Summary

|----|-----------------|--------------------|-------------------|

|Step|-2 Log likelihood|Cox & Snell R Square|Nagelkerke R Square|

|----|-----------------|--------------------|-------------------|

|1 |240.048a |.103 |.185 |

|---------------------------------------------------------------|

a Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.

3. Does that mean I need to ignore -2 Log likelihood method's results?

Classification Tablea

|------|----------------------------------|-----------------------------------------------------|

| |Observed |Predicted |

| | |----------------------------------|------------------|

| | |Notusing_free_insurance_services |Percentage Correct|

| | |------------------------------|---| |

| | |.0 |1.0| |

|------|------------------------------|---|------------------------------|---|------------------|

|Step 1|Notusing_free_insurance_services|.0 |293 |0 |100.0 |

| | |---|------------------------------|---|------------------|

| | |1.0|47 |1 |2.1 |

| |----------------------------------|------------------------------|---|------------------|

| |Overall Percentage | | |86.2 |

|-----------------------------------------------------------------------------------------------|

a The cut value is .500

Variables in the Equation

|---------------|------|----|-------|--|----|------|

| |B |S.E.|Wald |df|Sig.|Exp(B)|

|------|--------|------|----|-------|--|----|------|

|Step 0|Constant|-1.809|.156|134.964|1 |.000|.164 |

|--------------------------------------------------|

4. I should have taken some statistics classes so that I can understand the output, but does the output indicate what I estimated earlier(People with relatively low income, education and those who are younger(18 to 24) are the group who use free insurance services less compared to other groups)?

I tried to attach a text file with this output, but got the message "Sorry, the board attachment quota has been reached."

Thank you for helping me out.

could you try to upload your output file as an attachment?

Furthermore, in order to answer your question we need to know more about your variables.

E.g., income is a scale variable, with lower values indicating a lower income.

Furthermore, in order to answer your question we need to know more about your variables.

E.g., income is a scale variable, with lower values indicating a lower income.

Thanks Gerinel,

I have pasted again as "code"

**I appreciate your assistance and time.**

I tried to upload as MS-Word or RTF(Rich Text Format), but those type of files are not allowed. Then, I tried to upload a PDF and got the message "Sorry, the board attachment quota has been reached." How can I upload my output?GerineL wrote:could you try to upload your output file as an attachment?

I have pasted again as "code"

Code: Select all

```
Block 0: Beginning Block
Classification Table(a,b)
| |Observed |Predicted |
| | |----------------------------------|------------------|
| | |Notusing_free_insurance_services |Percentage Correct|
| | |------------------------------|---| |
| | |.0 |1.0| |
|------|------------------------------|---|------------------------------|---|------------------|
|Step 0|Notusing_free_insurance_services|.0 |293 |0 |100.0 |
| | |---|------------------------------|---|------------------|
| | |1.0|48 |0 |.0 |
| |----------------------------------|------------------------------|---|------------------|
| |Overall Percentage | | |85.9 |
|-----------------------------------------------------------------------------------------------|
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|-1.809|.156|134.964|1 |.000|.164 |
|--------------------------------------------------|
Variables not in the Equation
|-----------------------------------|------|--|----|
| |Score |df|Sig.|
|------|------------------|---------|------|--|----|
|Step 0|Variables |Age |12.310|7 |.091|
| | |---------|------|--|----|
| | |Age(1) |7.595 |1 |.006|
| | |---------|------|--|----|
| | |Age(2) |.104 |1 |.747|
| | |---------|------|--|----|
| | |Age(3) |.213 |1 |.644|
| | |---------|------|--|----|
| | |Age(4) |.948 |1 |.330|
| | |---------|------|--|----|
| | |Age(5) |4.997 |1 |.025|
| | |---------|------|--|----|
| | |Age(6) |2.392 |1 |.122|
| | |---------|------|--|----|
| | |Age(7) |.164 |1 |.685|
| | |---------|------|--|----|
| | |Gender(1)|9.617 |1 |.002|
| | |---------|------|--|----|
| | |Education|.258 |1 |.612|
| | |---------|------|--|----|
| | |Income |8.708 |1 |.003|
| |----------------------------|------|--|----|
| |Overall Statistics |29.755|10|.001|
|--------------------------------------------------|
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
|------------|----------|--|----|
| |Chi-square|df|Sig.|
|------|-----|----------|--|----|
|Step 1|Step |37.079 |10|.000|
| |-----|----------|--|----|
| |Block|37.079 |10|.000|
| |-----|----------|--|----|
| |Model|37.079 |10|.000|
|-------------------------------|
Model Summary
|----|-----------------|--------------------|-------------------|
|Step|-2 Log likelihood|Cox & Snell R Square|Nagelkerke R Square|
|----|-----------------|--------------------|-------------------|
|1 |240.048(a) |.103 |.185 |
|---------------------------------------------------------------|
a Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.
Classification Tablea
|------|----------------------------------|-----------------------------------------------------|
| |Observed |Predicted |
| | |----------------------------------|------------------|
| | |Notusing_free_insurance_services |Percentage Correct|
| | |------------------------------|---| |
| | |.0 |1.0| |
|------|------------------------------|---|------------------------------|---|------------------|
|Step 1|Notusing_free_insurance_services|.0 |293 |0 |100.0 |
| | |---|------------------------------|---|------------------|
| | |1.0|47 |1 |2.1 |
| |----------------------------------|------------------------------|---|------------------|
| |Overall Percentage | | |86.2 |
|-----------------------------------------------------------------------------------------------|
a The cut value is .500
Variables in the Equation
|-----------------|-------|---------|-----|--|-----|-------------|
| |B |S.E. |Wald |df|Sig. |Exp(B) |
|-------|---------|-----------------|-----|--|-----|-------------|
|Step 1a|Age | |1.578|7 |.979 | |
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(1) |19.509 |16141.860|.000 |1 |.999 |297074329.512|
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(2) |19.068 |16141.860|.000 |1 |.999 |191083831.399|
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(3) |18.916 |16141.860|.000 |1 |.999 |164082825.458|
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(4) |18.642 |16141.860|.000 |1 |.999 |124807540.072|
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(5) |.329 |17766.143|.000 |1 |1.000|1.389 |
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(6) |.386 |19221.419|.000 |1 |1.000|1.471 |
| |---------|-------|---------|-----|--|-----|-------------|
| |Age(7) |.711 |43313.214|.000 |1 |1.000|2.035 |
| |---------|-------|---------|-----|--|-----|-------------|
| |Gender(1)|-1.062 |.338 |9.899|1 |.002 |.346 |
| |---------|-------|---------|-----|--|-----|-------------|
| |Education|.327 |.153 |4.579|1 |.032 |1.386 |
| |---------|-------|---------|-----|--|-----|-------------|
| |Income |-.215 |.094 |5.255|1 |.022 |.807 |
| |---------|-------|---------|-----|--|-----|-------------|
| |Constant |-21.401|16141.860|.000 |1 |.999 |.000 |
|----------------------------------------------------------------|
a Variable(s) entered on step 1: Age, Gender, Education, Income.
```

Yes, income, age, education are such variables. Now, can you please advise on my questions?GerineL wrote: Furthermore, in order to answer your question we need to know more about your variables.

E.g., income is a scale variable, with lower values indicating a lower income.

Sorry again, wee keep coming back

I don't think all your variables are like that. For instance, if I look at age, it seems to consist of many variables.

hence, it is not a normal scale variable.

So please indicate - for each variable - what it consists of.

I don't think all your variables are like that. For instance, if I look at age, it seems to consist of many variables.

hence, it is not a normal scale variable.

So please indicate - for each variable - what it consists of.

Can I post an attachment?GerineL wrote:Sorry again, wee keep coming back

I removed the age as independent variable.GerineL wrote: I don't think all your variables are like that. For instance, if I look at age, it seems to consist of many variables.

hence, it is not a normal scale variable.

So please indicate - for each variable - what it consists of.

In SPSS22, I chose Analyze->Regression->Binary Logistic, then choose dependent variable as people having insurance who are not using free insurance services(like preventive services) who are coded as 1(whereas people having insurance and using free insurance services are coded as 0) for this variable, independent variable as education, income, Method as Enter. I did not choose anything in Save, Options, Bootstrap, Style

I have pasted the "variables in equation", "step 1".

Code: Select all

```
Variables in the Equation
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a Gender(1) -1.076 .330 10.643 1 .001 .341
Education .295 .129 5.224 1 .022 1.343
Income -.315 .090 12.175 1 .000 .730
Constant -1.789 .558 10.284 1 .001 .167
a Variable(s) entered on step 1: Gender, Education, Income.
```

2. I should have taken some statistics classes so that I can understand the output, but does the output indicate what I estimated earlier(People with relatively low income, education and males are the group who use free insurance services less compared to other groups)?

Thank you for your time and assistance.

http://www.ats.ucla.edu/stat/spss/output/logistic.htm

On this website, you see a step-by-step explanation of how to interpret these outcomes.

In your case, it seems like education will increase the likelihood of having insurance, income will decrease it.

Gender is hard to say, because you did not explain how this one is coded.

Again, it would be very helpful if you could explain how your data are coded, because interpretation of the results depends on that!!

On this website, you see a step-by-step explanation of how to interpret these outcomes.

In your case, it seems like education will increase the likelihood of having insurance, income will decrease it.

Gender is hard to say, because you did not explain how this one is coded.

Again, it would be very helpful if you could explain how your data are coded, because interpretation of the results depends on that!!

Thanks GerineL,

I re-estimated the model with education as categorical variable.

The categorical variables were coded in a way unclear to me by SPSS 22.

Female was given 1 and Male 0 though there were around 200 females and about 160 males.

Do you think they were coded properly?

**I appreciate your time and assistance.**

GerineL wrote:http://www.ats.ucla.edu/stat/spss/output/logistic.htm

On this website, you see a step-by-step explanation of how to interpret these outcomes.

In your case, it seems like education will increase the likelihood of having insurance, income will decrease it.

Gender is hard to say, because you did not explain how this one is coded.

Again, it would be very helpful if you could explain how your data are coded, because interpretation of the results depends on that!!

I re-estimated the model with education as categorical variable.

Code: Select all

```
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a Education 4.249 7 .751
Education(1) -19.877 28133.640 .000 1 .999 .000
Education(2) -19.993 11220.675 .000 1 .999 .000
Education(3) -1.042 .996 1.095 1 .295 .353
Education(4) -.415 .887 .219 1 .640 .660
Education(5) .232 1.048 .049 1 .825 1.261
Education(6) -.111 .891 .015 1 .901 .895
Education(7) .539 1.110 .236 1 .627 1.714
Income -.274 .093 8.610 1 .003 .760
Gender(1) -1.039 .333 9.703 1 .002 .354
Constant -.172 .973 .031 1 .859 .842
a Variable(s) entered on step 1: Education, Income, Gender.
```

The categorical variables were coded in a way unclear to me by SPSS 22.

Female was given 1 and Male 0 though there were around 200 females and about 160 males.

Do you think they were coded properly?

I don't know if using Eduaction like this makes sense, basically you now compare each of the seven conditions with one other condition.

Could you give us something like this?

Gender: dummycoded (1 = female 1 = male)

education: Ordinal (ranging from 1 = no education to 7 = masters degree)

etc.

Could you give us something like this?

Gender: dummycoded (1 = female 1 = male)

education: Ordinal (ranging from 1 = no education to 7 = masters degree)

etc.

Thanks GerineL,

Does Sig in SPSS output refer to p?

1. I realize p is the proportion(or probability) having an insurance at the given value of the explanatory variables. But, is the sig in SPSS output same as p meaning if sig is less than 0.05 for say age, then age significantly affects usage of preventive services? Am I understanding correctly?

2. Sorry, if this is naive, but does SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test(http://en.wikipedia.org/wiki/Wald_test)?

**I appreciate your time and assistance.**

I did not get this. I thought education was categorized as 7 levels Education(1) to Education(7). Can you please explain this more?GerineL wrote:I don't know if using Eduaction like this makes sense, basically you now compare each of the seven conditions with one other condition.

I will post it.GerineL wrote: Could you give us something like this?

Gender: dummycoded (1 = female 1 = male)

education: Ordinal (ranging from 1 = no education to 7 = masters degree)

etc.

Does Sig in SPSS output refer to p?

1. I realize p is the proportion(or probability) having an insurance at the given value of the explanatory variables. But, is the sig in SPSS output same as p meaning if sig is less than 0.05 for say age, then age significantly affects usage of preventive services? Am I understanding correctly?

2. Sorry, if this is naive, but does SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test(http://en.wikipedia.org/wiki/Wald_test)?

yes.p_s_ wrote: Does Sig in SPSS output refer to p?

Yes you are understanding correctly. Be careful with words like affect though, as you probably did not expermentally manipulate anything, you cannot draw conclusions about causality.1. I realize p is the proportion(or probability) having an insurance at the given value of the explanatory variables. But, is the sig in SPSS output same as p meaning if sig is less than 0.05 for say age, then age significantly affects usage of preventive services? Am I understanding correctly?

yes you are correct.2. Sorry, if this is naive, but does SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test(http://en.wikipedia.org/wiki/Wald_test)?

If I were you, I would look at some case-studies using logistic regression, such as this one:

http://core.ecu.edu/psyc/wuenschk/MV/Mu ... c-SPSS.PDF

Thanks Gerinel,

That means something is flawed with my analysis. I have posted results with sig values most of which are close to 1.
1. Could separating income(0 to 15999, then 16000 to 25999 and so on...), education(middle school, high school and so on), age into categories could have caused this?

2. How can I trace and fix the error?

3. For a regression analysis like mine, do I need to worry about SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test([url]http://en.wikipedia.org/wiki/Wald_test)?

I appreciate all your assistance and time.

That means something is flawed with my analysis. I have posted results with sig values most of which are close to 1.

Code: Select all

```
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a Age 2.196 6 .901
Age(1) 19.097 13651.701 .000 1 .999 196747485.390
Age(2) 18.727 13651.701 .000 1 .999 135881344.086
Age(3) 18.585 13651.701 .000 1 .999 117886225.083
Age(4) 17.635 13651.701 .000 1 .999 45569434.410
Age(5) -.511 15322.555 .000 1 1.000 .600
Age(6) .181 17048.756 .000 1 1.000 1.198
Gender(1) -1.058 .352 9.021 1 .003 .347
Education 6.994 7 .430
Education(1) -2.226 30163.899 .000 1 1.000 .108
Education(2) -19.990 9907.915 .000 1 .998 .000
Education(3) -2.017 1.181 2.916 1 .088 .133
Education(4) -1.561 1.098 2.021 1 .155 .210
Education(5) .543 1.234 .194 1 .660 1.721
Education(6) -1.015 1.055 .924 1 .336 .363
Education(7) -.603 1.233 .239 1 .625 .547
Income 7.259 8 .509
Income(1) .631 .862 .536 1 .464 1.880
Income(2) 1.089 .905 1.449 1 .229 2.972
Income(3) -.282 1.127 .063 1 .802 .754
Income(4) -1.021 1.161 .773 1 .379 .360
Income(5) -.699 1.427 .240 1 .624 .497
Income(6) .587 .996 .348 1 .555 1.799
Income(7) -19.720 9801.456 .000 1 .998 .000
Income(8) -19.130 10488.552 .000 1 .999 .000
Constant -19.042 13651.701 .000 1 .999 .000
a Variable(s) entered on step 1: Age, Gender, Education, Income.
```

2. How can I trace and fix the error?

3. For a regression analysis like mine, do I need to worry about SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test([url]http://en.wikipedia.org/wiki/Wald_test)?

I appreciate all your assistance and time.

These are all elements of the analysis, which are used to come to the conclusion about significance. I am not sure what you mean by "worry about these". It is common to report them though.p_s_ wrote:

1. Could separating income(0 to 15999, then 16000 to 25999 and so on...), education(middle school, high school and so on), age into categories could have caused this?Yes. If you separate income into groups like you indicated, this basically means that you assume that there will be no difference between an income of 1 or 15000, but there will be a difference between incomes of 25999 and 26000.

There are some cases in which it would make sense to separate into categories like you did, but usually using the variable as a continuous variable is the way to go. I assume that in your case, using the continuous variable would make much more sense. Can you indicate why you chose to make categories?

First of all, it could be that there just is no relation. That's why you perform the test, to see if there is a relation between your variables.2. How can I trace and fix the error?

Second, I think it will help a lot to use the variables continuously instead of creating categories.

3. For a regression analysis like mine, do I need to worry about SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test([url]http://en.wikipedia.org/wiki/Wald_test)?

Thanks Gerinel,

**I appreciate your assistance and time.**

I chose categories because I thought they cannot be differentiated from each other using a mathematical method. But, now I think only gender and education are categorical. Income and age can be continuous. Do you think this is proper?GerineL wrote:

1. Could separating income(0 to 15999, then 16000 to 25999 and so on...), education(middle school, high school and so on), age into categories could have caused this?

Yes. If you separate income into groups like you indicated, this basically means that you assume that there will be no difference between an income of 1 or 15000, but there will be a difference between incomes of 25999 and 26000.

There are some cases in which it would make sense to separate into categories like you did, but usually using the variable as a continuous variable is the way to go. I assume that in your case, using the continuous variable would make much more sense. Can you indicate why you chose to make categories?

Did you mean I should use all the variables continuously or only some like income, age?GerineL wrote:2. How can I trace and fix the error?

First of all, it could be that there just is no relation. That's why you perform the test, to see if there is a relation between your variables.

Second, I think it will help a lot to use the variables continuously instead of creating categories.

By "worry about these", I wanted to know, can I ignore them or do I need to know what they are indicate for my analysis and explain the reasons for it?GerineL wrote:3. For a regression analysis like mine, do I need to worry about SE mean Standard Error, df stand for Degrees of freedom, Wald refer to Wald test([url]http://en.wikipedia.org/wiki/Wald_test)?

These are all elements of the analysis, which are used to come to the conclusion about significance. I am not sure what you mean by "worry about these". It is common to report them though.

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