  Many epidemiologic studies forgo probability sampling and turn to
nonprobability volunteer-based samples because of cost, response burden, and
invasiveness of biological samples. However, finite population inference is
difficult to make from the nonprobability samples due to the lack of population
representativeness. Aiming for making inferences at the population level using
nonprobability samples, various inverse propensity score weighting (IPSW)
methods have been studied with the propensity defined by the participation rate
of population units in the nonprobability sample. In this paper, we propose an
adjusted logistic propensity weighting (ALP) method to estimate the
participation rates for nonprobability sample units. Compared to existing IPSW
methods, the proposed ALP method is easy to implement by ready-to-use software
while producing approximately unbiased estimators for population quantities
regardless of the nonprobability sample rate. The efficiency of the ALP
estimator can be further improved by scaling the survey sample weights in
propensity estimation. Taylor linearization variance estimators are proposed
for ALP estimators of finite population means that account for all sources of
variability. The proposed ALP methods are evaluated numerically via simulation
studies and empirically using the na\"ive unweighted National Health and
Nutrition Examination Survey III sample, while taking the 1997 National Health
Interview Survey as the reference, to estimate the 15-year mortality rates.
