11. Latent variable models for harmonization of data from different sources: application to UN sustainable development goals

Introduction

Data sharing by scientists, research organizations, and governments is on the rise, which enhances opportunities for secondary data analysis (Tenopir et al., 2011). However, pooling data from different sources often requires harmonizing measures prior to statistical analysis. Harmonization consists of placing measures of the same variable collected using different questionnaires on a shared metric. As will be illustrated in this project, latent variable modeling is a promising method for harmonizing data from different sources.

Data Analysis

To illustrate how data harmonization using latent variable modeling can be performed prior to the statistical analysis, data from the United Nations Demographic and Health Surveys (DHS) Program collected in 70 countries were used to evaluate whether education level, gender and marital status predicted acceptance of domestic violence. Acceptance of domestic violence was modeled as a latent factor capturing the common variance of the percentage of participants who endorsed six statements related to domestic violence of husbands against wives (e.g., "A husband is justified in hitting or beating his wife if she burns the food").
The complexity of the statistical model required a multi-step process. The data had a nested structure where the presence of multiple data points from the same country in the sample caused dependence of observations, and therefore needed to be modeled explicitly using a hierarchical model. Additionally, the presence of a latent factor in conjunction with the nested structure prevented the complete statistical model from converging for the available data. The analysis was divided into 2 steps: (1) a factor analysis estimating the individual scores for each demographic group across the 70 countries on the latent variable acceptance of domestic violence excluding predictors, and (2) the estimation of the full hierarchical model in which demographic variables predict the latent variable acceptance of domestic violence.

Results

In a model predicting acceptance of domestic violence using education level, gender, and marital status, findings indicated that only gender predicted attitudes toward domestic violence (holding education level and marital status constant). Specifically, holding education level and marital status constant, being a woman instead of a man led to significantly less acceptance of domestic violence.

Implications

Latent variable modeling was used to create a single index of acceptance of domestic violence. Using the index as the dependent variable in the statistical model and demographic variables as predictors, results suggest that holding education level and marital status constant, men are more accepting of domestic violence against women, and could thus benefit more from a prevention campaign designed to shift attitudes toward intimate partner violence.

latent variable modeling

UN sustainable development goals

harmonization

intimate partner violence

factor score regression