Replication Data for: Analisis Model Autoregressive Distributed Lag Pada Data Google Search Console Pesantren Online

This study aims to determine the analysis of visits to the Google Search Console Islamic Boarding School data. The website analyzed is asyafina.com, this study uses the Autoregressive Distributed Model with data from July 11, 2021 to February 14, 2022. The results of this study indicate that in the long term, impressions and position on Google have a significant relationship with the number of clicks to visit the web. While in the short term, these variables are not significant. This result is different when using the CTR approach: Position is significant in the short term. This result calls for content creators to use the CTR indicator if they focus on the short term, but if the target indicator is the number of visits, make it a long-term target, so keep on creating content for the long term. Keywords: Autoregressive Distributed Lag, Google Search Console, SEO, content, online Islamic Boarding School

本研究旨在针对谷歌搜索控制台（Google Search Console）中的伊斯兰寄宿学校相关数据开展访问量分析。本研究所分析的网站为asyafina.com，研究采用了自回归分布模型（Autoregressive Distributed Model），所用数据的时间跨度为2021年7月11日至2022年2月14日。本研究结果显示，从长期维度来看，谷歌搜索中的展示量与搜索排名与网站的点击访问量存在显著相关关系；而在短期维度下，上述变量与点击访问量的相关关系并不显著。若采用点击率（CTR，Click-Through Rate）分析方法，则所得结果有所不同：此时搜索排名在短期维度下呈现显著相关关系。该研究结果可为内容创作者提供实践参考：若以短期效果为优化目标，则可采用点击率作为评估指标；若以访问量作为核心优化目标，则应将其设定为长期目标，故而需坚持开展长期内容创作。关键词：自回归分布滞后模型（Autoregressive Distributed Lag）、谷歌搜索控制台（Google Search Console）、搜索引擎优化（SEO, Search Engine Optimization）、内容创作、线上伊斯兰寄宿学校