Why Weight?

I’ve spoken a bit about how using weights is important when analyzing national/statewide datasets. The weights are used so the sample generalizes to a particular population (note: we are interested in making inferences about the population, not the sample). This is important because at times, in national datasets, certain subpopulations (e.g., Hispanic or Asian students) are oversampled to ensure that the sample size is large enough for subgroup analysis. Without using weights, certain groups may be overrepresented (or underrepresented).

In my own work conducting statewide school climate surveys, principals are provided the option to, in order to lessen the administrative burden, 1) randomly sample 25 students per grade level (i.e., the random sampling [RS] option) or 2) have all students in the school answer the survey (achieve > 80% response rate or the whole grade [WG] option). The probability of selection in each school is different.

Example 1

Consider two schools of 100 students each: In school A, random sampling is used (n = 25) and in school B, the whole grade option is used (n = 100). In school A, the probability of selection is 25/100 = .25. Weights are the inverse of the probability of selection or 1/.25 = 4. In other word, each response is worth four students for school A. For school B, the weights are set to 1 since everyone responded to the survey. So for the entire sample (n = 125), the respondents will have different weights. If a measure of some outcome is taken, the average will be greatly influenced by the school that did the whole grade option [this is a simplified example but you get the point].

Example 2

Take another example. Let’s say we are sampling students in public (N = 1,000) and private schools (N = 10,000). We just randomly sample 100 students in each group (forget the school clustering for now). On average, the students in private school scored 100 on some test and students in public school scored 150. The average of both public and private school students or all students is 125.

Private Public Total
Population (N) 1,000 10,000 11,000
Sample (n) 100 100 200
Sampling Weight 10 100
Unweighted Mean 100 150 125
Weighted Mean 100 150 145.45

However, we know that the sampling rate for each group/strata is different so if we ignore that, we treat all responses equally. In reality, students in private schools had a 100/1000 = 10% chance of being selected. Students in public schools had a 100/10000 = 1% chance. The expansion weight is the inverse of the probability of being selected: for private schools, the weight is $\frac{1}{.10} = 10$ and for public school students, the sampling weight is $\frac{1}{.01} = 100$. If we use the weighted mean, the average score is $145.45$ vs. $125$ (see below).

The unweighted (regular) mean is $\bar{x} = \frac{\Sigma x}{n}$.

The weighted mean is $\bar{x}_{w} = \frac{\Sigma wgt \times x}{\Sigma wgt}$.

To show the difference:

sc <- c(rep(100, 100), rep(150,100))
wt <- c(rep(10,100), rep(100,100))
df <- data.frame(sc, wt)
mean(df$sc) #unweighted ## [1] 125 weighted.mean(df$sc, df$wt) #weighted mean ## [1] 145.4545 Example 3 I’ll illustrate using simulated data (this time adding some nesting). I’ll also show how to get some basic weighted descriptive statistics. Simulate data with weights library(nlme) library(lme4) library(dplyr) library(jtools) ### can ignore this section, what is important is the dat file at the end l2s <- 10 #number of clusters l1 <- 20 #number of units per cluster G01 <- .3 #effect L2 G11 <- .5 #effect L1 set.seed(12345) tmp2 <- rnorm(l2s, 0, 1) tmp3 <- rep(c(0,1), each = 5) W2 <- rep(tmp2, each = l1) #l2 predictor FEM <- rbinom(l2s * l1, 1, .5) #gender l1 X1 <- rnorm(l2s * l1, 0, 2) #l1 predictor e2 <- rep(rnorm(l2s, 0, 1), each = l1) #l2 error grp <- rep(1:l2s, each = l1) e1 <- rnorm(l2s * l1, 0, 2) #this controls the ICC y <- G01 * W2 + G11 * X1 + FEM * .4 + e2 + e1 wts <- rep(c(10, 10, 5, 5, 1), each = 40) #these are the weights #in this case, just place them after to show differences #weights could result in: did not use them for the creation of y dat <- data.frame(y, X1, W2, FEM, grp, wts) #this is our dataset View descriptives Simple descriptives can be inspected with or without weights: mean(dat$y)
## [1] 0.5997105
weighted.mean(dat$y, dat$wts)
## [1] 0.8253441
#difference of ~0.23 pts

### by gender (FEMale = 1)
dat %>%
group_by(FEM) %>%
summarise(m.u = mean(y),
m.w = weighted.mean(y, wts))
## # A tibble: 2 x 3
##     FEM   m.u   m.w
##   <int> <dbl> <dbl>
## 1     0 0.252 0.171
## 2     1 0.862 1.31

Based on the weights, the differences between weighted and unweighted means increased (.25 vs .86: Unweighted) vs (.17 vs 1.31: Weighted).

Now this can be viewed one variable at time but to simplify this, I can use the tableone package which can give descriptives, by group, using weights and without weights. For unweighted descriptives (stratified by gender):

library(tableone)
vars <- c('y', 'X1', 'W2')
t1 <- CreateTableOne(data = dat, vars = vars, strata = 'FEM')
print(t1, smd = T) #smd = T to show standardized mean differences
##                 Stratified by FEM
##                  0            1            p      test SMD
##   n                 86          114
##   y (mean (sd))   0.25 (2.26)  0.86 (2.92)  0.109       0.234
##   X1 (mean (sd)) -0.10 (1.96)  0.17 (1.93)  0.327       0.140
##   W2 (mean (sd)) -0.07 (0.73) -0.18 (0.81)  0.334       0.139

For weighted descriptives (note: requires the survey package to incorporate the weights):

library(survey)
des <- svydesign(ids = ~grp, weights = ~wts, data = dat)
t2 <- svyCreateTableOne(data = des, strata = "FEM", vars = vars)
print(t2, smd = T)
##                 Stratified by FEM
##                  0             1             p      test SMD
##   n              527.00        713.00
##   y (mean (sd))    0.17 (2.28)   1.31 (2.80)  0.051       0.446
##   X1 (mean (sd))  -0.18 (1.95)   0.26 (2.07)  0.331       0.219
##   W2 (mean (sd))   0.13 (0.68)  -0.04 (0.75)  0.124       0.237

We can see greater differences among the variables. This is useful.

Analyze data using multilevel models

Since we have nested data, we may also analyze the data using a multilevel model. We can begin by viewing the unconditional intraclass correlation coefficient (ICC).

library(lme4)
m0.uw <- lmer(y ~ (1|grp), data = dat) #using lme4
m0.w <- update(m0.uw, weights = wts) #with weights
summary(m0.uw)
## Linear mixed model fit by REML ['lmerMod']
## Formula: y ~ (1 | grp)
##    Data: dat
##
## REML criterion at convergence: 918
##
## Scaled residuals:
##      Min       1Q   Median       3Q      Max
## -2.56989 -0.60085  0.05237  0.56032  2.70286
##
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  grp      (Intercept) 2.110    1.453
##  Residual             5.201    2.281
## Number of obs: 200, groups:  grp, 10
##
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   0.5997     0.4868   1.232
summary(m0.w) 
## Linear mixed model fit by REML ['lmerMod']
## Formula: y ~ (1 | grp)
##    Data: dat
## Weights: wts
##
## REML criterion at convergence: 977.9
##
## Scaled residuals:
##     Min      1Q  Median      3Q     Max
## -2.9805 -0.5211  0.0283  0.4675  3.3282
##
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  grp      (Intercept)  1.857   1.363
##  Residual             34.101   5.840
## Number of obs: 200, groups:  grp, 10
##
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   0.6469     0.4793    1.35

Based on the random effects, the unconditional ICC of the unweighted model is .29. For the weighted model, the ICC is .05. That’s a big difference!

Now, if we examine results with and without weights using a full model:

m1.uw <- lmer(y ~ W2 + X1 + FEM + (1|grp), data = dat) #using lme4
m1.w <- update(m1.uw, weights = wts)
#export_summs(m1.uw, m1.w, model.names = c('UnW', 'W'), digits = 3)
library(stargazer)
stargazer(m1.uw, m1.w, type = 'text', digits = 3,
no.space = T, intercept.bottom = F,
star.cutoffs = c(.05, .01, .001),
column.labels = c('UnW', 'W'))
##
## ==================================================
##                          Dependent variable:
##                     ------------------------------
##                                   y
##                           UnW             W
##                           (1)            (2)
## --------------------------------------------------
## Constant                 0.320          0.180
##                         (0.522)        (0.512)
## W2                       0.365          0.318
##                         (0.636)        (0.624)
## X1                     0.636***        0.680***
##                         (0.069)        (0.065)
## FEM                      0.520         0.768**
##                         (0.277)        (0.268)
## --------------------------------------------------
## Observations              200            200
## Log Likelihood         -423.252        -441.384
## Akaike Inf. Crit.       858.505        894.767
## Bayesian Inf. Crit.     878.295        914.557
## ==================================================
## Note:                *p<0.05; **p<0.01; ***p<0.001

In this case, relationship of gender (FEM = 1) is higher in the weighted version.

Very quick and important sidenote

With R, the two main packages to use when estimating multilevel models are lme4 and nlme. Both are great though researchers should be careful when using weights, especially with nlme.

library(nlme)
nlme.u <- lme(y ~ W2 + X1 + FEM, random = ~1|grp, data = dat) #using nlme
nlme.w1 <- update(nlme.u, weights = ~wts) #incorrect
nlme.w2 <- update(nlme.u, weights = ~1/wts) #correct
stargazer(nlme.u, nlme.w1, nlme.w2, m1.w, type = 'text',
digits = 3, no.space = T, intercept.bottom = F,
star.cutoffs = c(.05, .01, .001),
column.labels = c('nlme.UnW', 'nlme.W [wrong]' ,'nlme.W [correct]', 'lmer.W'),
dep.var.caption = '', dep.var.labels.include = F,
model.names = F)
##
## =====================================================================
##                     nlme.UnW nlme.W [wrong] nlme.W [correct]  lmer.W
##                       (1)         (2)             (3)          (4)
## ---------------------------------------------------------------------
## Constant             0.320       0.645           0.180        0.180
##                     (0.522)     (0.539)         (0.512)      (0.512)
## W2                   0.365       0.398           0.318        0.318
##                     (0.636)     (0.648)         (0.624)      (0.624)
## X1                  0.636***    0.485***        0.680***     0.680***
##                     (0.069)     (0.079)         (0.065)      (0.065)
## FEM                  0.520       -0.071         0.768**      0.768**
##                     (0.277)     (0.295)         (0.268)      (0.268)
## ---------------------------------------------------------------------
## Observations          200         200             200          200
## Log Likelihood      -423.252    -476.987        -441.384     -441.384
## Akaike Inf. Crit.   858.505     965.973         894.767      894.767
## Bayesian Inf. Crit. 878.174     985.642         914.436      914.557
## =====================================================================
## Note:                                   *p<0.05; **p<0.01; ***p<0.001

Note the way weights are specified. For sampling weights, it has to be written as ~1/weight when using nlme. The results are the same as when lmer was used (see Models 3 & 4; just repeated). It is incorrect to use just ~weight as these are not sampling weights (see Model 2 results w/c are off).

NOTE: this is for functions that use the nlme package. That goes for the glmmPQL function in the MASS package.

If you compare this with what SAS generates using PROC MIXED, you will get the same results.

proc mixed data = a; #imported into dataset a
class grp;
weight wts;
model y = W2 X1 FEM /solution ddfm = kr;
random intercept / subject = grp;
run;

Weighting at two levels (WeMix)

WeMix is a new package for running mixed models with weights at more than one level (the only package that I know of). There is still some debate on how to use weight at the different levels.

Testing whether sampling weights are needed

DuMouchel and Duncan (1983 in JASA; Using Sample Survey Weights in Multiple Regression Analyses of Stratified Samples) compared results between weighted and unweighted analyses. Long implemented this test in the jtools package. This works with an lm object. A statistically significant F test suggests that weights are needed. (note: I have never actually used this particular test in my own work but is interesting to know such a test exists). For a review of tests to see if weights are useful, see Bollen et al.’s (2016) article. Basically the two general types of tests are Hausman (1978) tests (i.e., difference in coefficients [DC] tests) and weighted association (WA) tests.

library(jtools)
tt1 <- lm(y ~ W2 + X1 + FEM, data = dat)
weights_tests(model = tt1, weights = wts, data = dat, sims = 100)
## DuMouchel-Duncan test of model change with weights
##
## F(4,192) = 6.867
## p = 0
##
## Lower p values indicate greater influence of the weights.
##
## Standard errors: OLS
##
## |            |  Est.| S.E.| t val.|    p|
## |:-----------|-----:|----:|------:|----:|
## |(Intercept) |  1.18| 0.51|   2.33| 0.02|
## |W2          |  2.19| 0.56|   3.94| 0.00|
## |wts         | -0.07| 0.07|  -1.02| 0.31|
## |X1          |  0.44| 0.18|   2.48| 0.01|
## |FEM         | -1.06| 0.64|  -1.65| 0.10|
## |W2:wts      | -0.35| 0.09|  -3.85| 0.00|
## |wts:X1      |  0.03| 0.02|   1.19| 0.24|
## |wts:FEM     |  0.22| 0.09|   2.38| 0.02|
##
## ---
## Pfeffermann-Sverchkov test of sample weight ignorability
##
## Residual correlation = 0.12, p = 0.07
## Squared residual correlation = -0.10, p = 0.19
## Cubed residual correlation = 0.10, p = 0.12
##
## A significant correlation may indicate biased estimates
## in the unweighted model.

#END

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