Summary information

Study title

Replication Code: Gummer & Oehrlein (2024): "Using Google Trends Data to Study High-Frequency Search Terms: Evidence for a Reliability-Frequency Continuum." Social Science Computer Review

Creator

Gummer, Tobias ( GESIS - Leibniz Institut für Sozialwissenschaften)
Oehrlein, Anne-Sophie ( GESIS - Leibniz Institut für Sozialwissenschaften)

Study number / PID

10.7802/2748 (GESIS)

10.7802/2748 (DOI)

Data access

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Series

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Abstract

Based on the data and the syntax file, the results reported in the cited article can be replicated. Google Trends (GT) data are increasingly used in the social sciences and adjacent fields. However, previous research on the quality of GT data has raised concerns regarding their reliability. In the present study, we investigated whether reliability differs between low- and high-frequency search terms. In other words, we explored the existence of a reliability-frequency continuum in GT data. Our study adds to previous research by investigating a more comprehensive set of search terms and different aspects of reliability (e.g., differences in relative search volume distributions, correctly identified maxima). For this purpose, we collected samples of GT data for ten high- and two low-frequency search terms. We obtained one real-time sample and 62 non-realtime samples per search term (30 non-realtime samples for low-frequency search terms). Data collection was restricted to search data for Germany. Our data support the existence of a reliability-frequency continuum—low-frequency search terms are subject to greater reliability issues compared to high-frequency search terms. Based on our findings, we have derived practical recommendations for the use of GT data and have outlined future research opportunities.

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Methodology

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Universe

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Access

Publisher

GESIS Data Archive for the Social Sciences

Publication year

2024

Terms of data access

Free access (with registration) - The research data can be downloaded by registered users.

Related publications

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