
@article {4124,
	title = {What predicts homonegativity in Southeast Asian countries? Evidence from the World Values Survey},
	journal = {Psychology \& Sexuality},
	year = {2023},
	abstract = {Negative attitudes towards lesbian women and gay men continue to exist in Southeast Asian countries. This study identified predictors of homonegativity that are generally consistent across the countries in the region. Using data from the seventh round of World Values Survey, we obtained parsimonious country-level logistic regression models for six of the 11 Southeast Asian countries: Indonesia (n = 3,200), Malaysia (n = 1,313), Myanmar (n = 1,200), the Philippines (n = 1,200), Thailand (n = 1,500), and Vietnam (n = 1,200). Results suggest that four values and one demographic variable are consistent predictors of homonegativity in Southeast Asia. Endorsements of equality, choice, and agnosticism were found to be consistent predictors of lower levels of homonegative attitudes, while the opposite was observed for endorsement of relativism and older people. That there are some consistent cultural predictors of lower levels of homonegativity may suggest a common emancipative logic in Southeast Asia. On the whole, however, the findings suggest that there may be no uniform (Southeast) Asian values system that constitutes sexual prejudice. This foregrounds the need for more contextually-sensitive and culturally-informed models of homonegativity to understand why negative attitudes persist in some countries but not in others, and to also guide the crafting of interventions that are more relevant to each country.},
	url = {https://doi.org/10.1080/19419899.2023.2296591 },
	author = {Xavier Javines Bilon and Jose Antonio R. Clemente}
}
@article {4125,
	title = {Normality and significance testing in simple linear regression model for large sample sizes: a simulation study},
	journal = {Communications in Statistics - Simulation and Computation},
	volume = {52},
	number = {6},
	year = {2023},
	pages = {2781{\textendash}2797},
	abstract = {Data analysis techniques that rely on standard statistical tools and algorithms often encounter problems when dealing with data sets that have large sample sizes. In this study, two statistical tests done in conducting simple linear regression analysis were revisited. In particular, the study simulated the effects of large sample sizes and amount of contamination in the data due to non-sampling errors on the false positive rate of the Kolmogorov-Smirnov (K-S) test in testing for normality of error terms. The study also characterized the effects of varying sample size and amount of contamination in the data on the false negative rate of the t-test in testing the significance of a regression coefficient. Lastly, an optimality index was developed to determine the sample sizes and the values of the percent noise at which both the false positive rate of the K-S test and the false negative rate of the t-test are minimized.},
	url = {https://doi.org/10.1080/03610918.2021.1916824},
	author = {Xavier Javines Bilon}
}
@article {3055,
	title = {Evaluation of sampling methods for content analysis of Facebook data},
	journal = {The Philippine Statistician},
	year = {2020},
	abstract = {A methodological challenge for researchers performing content analysis on social media data involves deciding on a sampling procedure for obtaining content to be analyzed with least sampling error. The study used and recommended two different kinds of elementary unit{\textemdash}post and day{\textemdash}that allow probability sampling of Facebook data, regardless of whether the sampling frame of all posts within the time period of interest is obtainable. Four sampling designs for post as elementary unit and five for day as elementary unit{\textemdash}including three commonly used sampling options for contentanalysis: simple random sampling without replacement (SRSWOR), constructed week sampling, and consecutive day sampling{\textemdash}were employed on Facebook data mined from Mocha Uson Blog from 2010 to 2018. Estimates for parameters, such as measures of user engagement and proportions of topic-related posts, were obtained at increasing sample sizes. Sampling designs for each elementary unit were evaluated by comparing the normalized area under the coefficient of variation curve (NAUCV) over the different sample sizes. For post as elementary unit, with content type as the stratification variable, stratified random sampling (StRS) using Neyman allocation based on total user engagement isrecommended (average NAUCV = 31.28\%). For day as elementary unit, SRSWOR is recommended (average NAUCV = 42.31\%).},
	url = {https://www.psai.ph/tps_details.php?id=125},
	author = {Xavier Javines Bilon and Jose Antonio R. Clemente}
}
