Statalist The Stata Listserver


[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: Survey data - using linear test and the nosvyadjust


From   "Jason Ferris" <[email protected]>
To   <[email protected]>
Subject   st: Survey data - using linear test and the nosvyadjust
Date   Mon, 29 Jan 2007 12:56:07 +1100

Hi all,
Using Stata9 and survey data I am having a few concerns about the
utilisation of nosvyadjust when running test models.

With my data I have the following design specs:
pweight: pps
VCE: linearized
Strata 1: sex
SU 1: <observations>
FPC 1: <zero>

I ran a tab:
svy: tab ec12 qmc22, count col pearson null wald noadjust
Number of strata   =         1               Number of obs      =
4364
Number of PSUs     =      4364               Population size    =
4365.2309
                                             Design df          =
4363
-------------------------------
          |  other p. pain-12m 
     ec12 |    no    yes  Total
----------+--------------------
       no |  3368  841.9   4210
          | .9684  .9494  .9645
          | 
      yes |   110  44.85  154.8
          | .0316  .0506  .0355
          | 
    Total |  3478  886.8   4365
          |     1      1      1
-------------------------------
  Key:  weighted counts
        column proportions

  Pearson:
    Uncorrected   chi2(1)         =    7.4145
    D-B (null)    F(1, 4363)      =    3.7006     P = 0.0545
    Design-based  F(1, 4363)      =    4.9500     P = 0.0261

  Wald (Pearson):
    Unadjusted    chi2(1)         =    3.6951
    Unadjusted    F(1, 4363)      =    3.6951     P = 0.0546
    Adjusted      F(1, 4363)      =    3.6951     P = 0.0546

Then I ran the svy: proportion of the model and a linear hypothesis test
(with and without nosvyadjust):

svy: proportion ec12, over(qmc22)
--------------------------------------------------------------
             |             Linearized         Binomial Wald
        Over | Proportion   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
no           |
          no |   .9683775   .0034443      .9616249    .9751301
         yes |   .9494275   .0091864      .9314175    .9674375
-------------+------------------------------------------------
yes          |
          no |   .0316225   .0034443      .0248699    .0383751
         yes |   .0505725   .0091864      .0325625    .0685825
--------------------------------------------------------------

test [no]no=[no]yes
Adjusted Wald test
 ( 1)  [no]no - [no]yes = 0
       F(  1,  4363) =    3.73
            Prob > F =    0.0535


test [no]no=[no]yes, nosvyadjust
Unadjusted Wald test
 ( 1)  [no]no - [no]yes = 0
       F(  1,  4363) =    3.73
            Prob > F =    0.0535


1. As this is survey data, when I do linear tests SHOULD I use the
nosvyadjust options?

2. Why is my D-B model F(1, 4363) = 4.9500; P = 0.0261 significant yet
the linear test is not: F(1,  4363) = 3.73; Prob > F = 0.0535

3. This difference becomes a little concerning when one of my variables
say year5 has more than 2 categories (e.g., 5 year groups 16-20, 21-25
etc).  When I want to test for linear differences between groups I
question the results of the linear test (see below).  In particular the
proportions test for group 2 (_prop_2) has overlapping CIs.  Yet the
linear tests suggest that these are statistically different (this is the
same for using or not using the option nosvyadjust; see below).  This
raises the question for me: Can I use the linear test output to
highlight which groups are contributing to any overall differences?

svy: proportion year5, over(qmc16)
--------------------------------------------------------------
             |             Linearized         Binomial Wald
        Over | Proportion   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
_prop_1      |
          no |   .1029562   .0143045      .0749072    .1310051
         yes |   .1531804   .0108347      .1319352    .1744257
-------------+------------------------------------------------
_prop_2      |
          no |   .0917431   .0122425      .0677374    .1157488
         yes |   .1311121   .0087642      .1139269    .1482972
-------------+------------------------------------------------
_prop_3      |
          no |   .1202854   .0117523       .097241    .1433298
         yes |   .1224578   .0076194      .1075173    .1373983
-------------+------------------------------------------------
_prop_4      |
          no |   .1569827   .0127744      .1319341    .1820312
         yes |   .1557767   .0082218       .139655    .1718985
-------------+------------------------------------------------
_prop_5      |
          no |   .1712538    .012996      .1457708    .1967368
         yes |   .1691908   .0085773      .1523722    .1860095
-------------+------------------------------------------------
_prop_6      |
          no |   .2038736   .0154416      .1735951    .2341521
         yes |   .1639983   .0090101      .1463308    .1816657
-------------+------------------------------------------------
_prop_7      |
          no |   .1529052   .0140525      .1253505    .1804599
         yes |   .1042839   .0072473      .0900731    .1184947
-------------+------------------------------------------------ 


test ([_prop_1]no=[_prop_1]yes) ([_prop_2]no=[_prop_2]yes)
([_prop_3]no=[_prop_3]yes) ([_prop_4]no=[_prop_4]yes)
([_prop_5]no=[_prop_5]yes) ([_prop_6]no=[_prop_6]yes)
([_prop_7]no=[_prop_7]yes), mtest(noadjust)

---------------------------------------
       |  F(df,2695)     df       p
-------+-------------------------------
  (1)  |        7.83      1     0.0052 #
  (2)  |        6.84      1     0.0090 #
  (3)  |        0.02      1     0.8768 #
  (4)  |        0.01      1     0.9367 #
  (5)  |        0.02      1     0.8946 #
  (6)  |        4.97      1     0.0258 #
  (7)  |        9.46      1     0.0021 #
-------+-------------------------------
  all  |        4.12      6     0.0004
---------------------------------------

Sorry about the output but I thought it would provide some insight.
Jase

Jason Ferris
Research Officer
Australian Research Centre in Sex, Health and Society
Level 1, 215 Franklin St
Melbourne
VIC 3000
P: 61 (0)3 9285 5282
F: 61 (0)3 9285 5220


*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



© Copyright 1996–2024 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index