Measuring religious morality using very limited poll responses: Implementing "big-data analytics" to small data

TL;DR

This paper demonstrates how to construct a parametric, interval-level measure of religious morality from limited opinion poll responses using item response modeling, enabling more advanced statistical analysis and comparison across studies.

Contribution

It introduces a novel application of item response modeling to small poll data, creating a scalable measure of religious morality from minimal responses.

Findings

Developed a parametric scale from four poll questions.

Produced interval-level measures for religious morality.

Provided item parameters for future research adoption.

Abstract

Opinion polls remain among the most efficient and widespread methods to capture psycho-social data at large scales. However, there are limitations on the logistics and structure of opinion polls that restrict the amount and type of information that can be collected. As a consequence, data from opinion polls are often reported in simple percentages and analyzed non-parametrically. In this paper, response data on just four questions from a national opinion poll were used to demonstrate that a parametric scale can be constructed using item response modeling approaches. Developing a parametric scale yields interval-level measures which are more useful than the strictly ordinal-level measures obtained from Likert-type scales common in opinion polls. The metric that was developed in this paper, a measure of religious morality, can be processed and used in a wider range of statistical analyses compared to conventional approaches of simply reporting percentages at item-level. Finally, this paper reports the item parameters so that researchers can adopt these items to future instruments and place their own results on the same scale, thereby allowing responses from future samples to be compared to the results from the representative data in this paper.