Author: Patrick van Kessel

The pros and cons of using professional translators vs. Google Translate to analyze open-ended survey responses

Pew Research Center recently fielded a cross-national survey in which people around the world described, in their own words, where they find meaning in life. The analysis plan for this project hinged on developing a closed-ended codebook and applying it to nearly 19,000 open-ended responses, drawn from 17 societies and spanning 12 languages.  In this post, we provide more background about this project and look more closely at a key question we faced along the way: whether to use professional translators or Google Translate to help make sense of the many thousands of open-ended responses we received.

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Where Americans find meaning in life has changed over the past four years

Americans have evolved in where they find meaning in their lives, according to a new Pew Research Center analysis of surveys conducted in September 2017 and February 2021. In both years, the Center asked a representative sample of U.S. adults to answer the following question in their own words: “What about your life do you currently find meaningful, fulfilling or satisfying? What keeps you going and why?”

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Where people around the world find meaning in life

In the spring of 2021 – as COVID-19 raged across many parts of the world – we asked nearly 19,000 adults in 17 publics where they find meaning in their lives and what keeps them going. While there are some similarities between the places surveyed – for example, family is a top source of meaning in most places – there are also numerous differences that highlight the uniqueness of each culture and public. We encourage you to discover these patterns using the interactive below, focusing either on a source of meaning or on one of the 17 publics surveyed. You can also explore a selection of the detailed, rich quotations provided by respondents around the world.

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Are topic models reliable or useful?

For now, the evidence suggests that even with intense curation, scrutiny and validation, topic models are not particularly well-suited for classification. Instead, their strength lies in exploratory analysis, where they can serve as tools of suggestion: Topic models can help guide researchers toward more curated measurement instruments informed by a human understanding of the data, its context and linguistic semantics more broadly.

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Both Republicans and Democrats cite masks as a negative effect of COVID-19, but for very different reasons

The COVID-19 outbreak has upended life across the United States and exposed growing divisions between supporters of the two major political parties. And when Americans are asked to describe in their own words how the outbreak has affected them negatively, no topic divides Democrats and Republicans more than the subject of masks, according to a new Pew Research Center analysis of survey findings collected in late August and early September.

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