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RecommendationsIn SpeedCurve, all the performance optimization recommendations you see on your Vitals and Improve dashboards – as well as in your synthetic test details – are badged so you can see which Web Vital they affect. When you fix those issues, you should see improvements in your Vitals and Lighthouse scores.Interaction to Next Paint (INP)Interaction to next paint is a responsiveness metric that replaces First Input Delay as a Core Web Vital. Like it's predecessor, INP measures real user interactions and can only be measured with RUM tools.A few technicalities about INP that you may care about:The user input required for INP is defined as a click/tap or key press. It doesn't include scroll or zoom.INP can be measured in traditional applications as well as SPAs.INP uses the Event Timing API which is supported in Chrome and Firefox (not available for Safari)What makes INP slower?Long-running JavaScript event handlersInput delay due to Long Tasks blocking the main threadPoorly performing JavaScript frameworksPage complexity leading to presentation delayLike FID, input delay can happen when the browser's main thread is too busy to respond to the user. Most commonly, this is due to the browser being busy parsing and executing large JavaScript files. There's a lot of unnecessary JS on many pages, and JS files have gotten bigger over the years. The more JS on your page, the more potential for slow INP times.How to investigate INP issuesWhile it's possible to simulate interactions in synthetic, INP can only be measured in RUM. This makes it fairly simple to track, but difficult to investigate. We recommend measuring Total Blocking Time (TBT) in your synthetic monitoring, alongside measuring INP in RUM. Additionally, you can use Interaction (IX) Elements in SpeedCurve RUM to help isolate the most common elements users are interacting with. Look for more diagnostic capability for INP in future releases.How to improve INPOptimize poorly performing event handlersEliminate Long Tasks by breaking up long-running code into smaller async tasksReduce overall number of resource requestsOptimize third parties, e.g., defer "below-the-fold" and other non-essential scriptsMinimize CPU main thread activity, e.g., use a web worker to run JS on a background threadCode splitting, i.e., minimize the amount of script that needs to be parsed and compiled by splitting JavaScript bundles to only send the code needed for the initial routeReduce DOM and layout complexity to reduce presentation delayMore: Optimize Interaction to Next PaintTotal Blocking Time (TBT)Total Blocking Time

ExampleView outputDownload inputDownload dataView Monte Carlo outputDownload Monte Carlo input5.1: CFA with continuous factor indicatorsex5.1ex5.1.inpex5.1.datmcex5.1mcex5.1.inp5.2: CFA with categorical factor indicatorsex5.2ex5.2.inpex5.2.datmcex5.2mcex5.2.inp5.3: CFA with continuous and categorical factor indicatorsex5.3ex5.3.inpex5.3.datmcex5.3mcex5.3.inp5.4: CFA with censored and count factor indicatorsex5.4ex5.4.inpex5.4.datmcex5.4mcex5.4.inp5.5: Generalized partial credit modelex5.5ex5.5.inpex5.5.datmcex5.5mcex5.5.inp5.5: Two-parameter logistic IRT model (part 2)ex5.5 (part 2)ex5.5.inp (part 2)ex5.5.dat (part 2)mcex5.5 (part 2)mcex5.5.inp (part 2)5.5: Three-parameter logistic IRT model (part 3)ex5.5 (part 3)ex5.5.inp (part 3)ex5.5.dat (part 3)mcex5.5 (part 3)mcex5.5.inp (part 3)5.5: Four-parameter logistic IRT model (part 4)ex5.5 (part 4)ex5.5.inp (part 4)ex5.5.dat (part 4)mcex5.5 (part 4)mcex5.5.inp (part 4)5.6: Second-order factor analysisex5.6ex5.6.inpex5.6.datmcex5.6mcex5.6.inp5.7: Non-linear CFAex5.7ex5.7.inpex5.7.datmcex5.7mcex5.7.inp5.8: CFA with covariates (MIMIC) with continuous factor indicatorsex5.8ex5.8.inpex5.8.datmcex5.8mcex5.8.inp5.9: Mean structure CFA for continuous factor indicatorsex5.9ex5.9.inpex5.9.datmcex5.9mcex5.9.inp5.10: Threshold structure CFA for categorical factor indicatorsex5.10ex5.10.inpex5.10.datmcex5.10mcex5.10.inp5.11: SEM with continuous factor indicatorsex5.11ex5.11.inpex5.11.datmcex5.11mcex5.11.inp5.12: SEM with continuous factor indicators and an indirect effect for factorsex5.12ex5.12.inpex5.12.datmcex5.12mcex5.12.inp5.13: SEM with continuous factor indicators and an interaction between two factorsex5.13ex5.13.inpex5.13.datmcex5.13mcex5.13.inp5.14: Multiple group CFA with covariates (MIMIC) with continuous factor indicators and no mean structureex5.14ex5.14.inpex5.14.datmcex5.14mcex5.14.inp5.15: Multiple group CFA with covariates (MIMIC) with continuous factor indicators and a mean structureex5.15ex5.15.inpex5.15.datmcex5.15mcex5.15.inp5.16: Multiple group CFA with covariates (MIMIC) with categorical factor indicators and a threshold structureex5.16ex5.16.inpex5.16.datmcex5.16mcex5.16.inp5.17: Multiple group CFA with covariates (MIMIC) with categorical factor indicators and a threshold structure using the Theta parameterizationex5.17ex5.17.inpex5.17.datmcex5.17mcex5.17.inp5.18: Two-group twin model for continuous outcomes where factors represent the ACE componentsex5.18ex5.18.inpex5.18.datmcex5.18mcex5.18.inp5.19: Two-group twin model for categorical outcomes where factors represent the ACE componentsex5.19ex5.19.inpex5.19.datmcex5.19mcex5.19.inp5.20: CFA with parameter constraintsex5.20ex5.20.inpex5.20.datmcex5.20mcex5.20.inp5.21: Two-group twin model for continuous outcomes using parameter constraintsex5.21ex5.21.inpex5.21.datmcex5.21mcex5.21.inp5.22: Two-group twin model for categorical outcomes using parameter constraintsex5.22ex5.22.inpex5.22.datmcex5.22mcex5.22.inp5.23: QTL sibling model for

Everything you need to know about Core Web Vitals – from SEO and business impact to how to continuously monitor, catch regressions, and fix issues with each Vital.What are Core Web Vitals?Core Web Vitals is a Google initiative, launched in early 2020, that is intended to focus on measuring performance from a user-experience perspective. Core Web Vitals is (currently) a set of three metrics – Largest Contentful Paint, First Input Delay, and Cumulative Layout Shift – that are intended to measure the loading, interactivity, and visual stability of a page.Largest Contentful Paint (LCP) – When the largest visual element on the page renders. LCP is measurable with both synthetic and real user monitoring (RUM).Cumulative Layout Shift (CLS) – How visually stable a page is. CLS is a formula-based metric that takes into account how much a page's visual content shifts within the viewport, combined with the distance that those visual elements shifted. The human-friendly definition is that CLS helps you understand how likely a page is to deliver a janky, unpleasant experience to viewers. CLS can be measured with synthetic and RUM.Interaction to Next Paint (INP) – How quickly a page responds to a user interaction. INP measures input delay, running time of event handlers and presentation delay. A lot of INP delays happen when the browser's main thread is too busy to respond to the user. Commonly, this is due to the browser being busy parsing and executing large JavaScript files. Because INP measures real user interactions, it can only be measured with RUM tools.Total Blocking Time (TBT) – How responsive a page is. While Total Blocking Time is a Web Vital, it's not a Core Web Vital. It bears discussion here as it is a companion metric to INP. TBT measures the total amount of time between First Contentful Paint (FCP) and Time to Interactive (TTI) when the main thread was blocked for long enough to prevent input responsiveness. TBT is measured in synthetic testing tools.It's important to make the distinction between Core Web Vitals and Web Vitals. Core Web Vitals focus on user experience metrics. They are a subset of Web Vitals – including Time to First Byte and Total Blocking Time – that serve as supplemental metrics for diagnosing specific performance issues.Core Web Vitals and SEOCore Web Vitals are among the page experience signals that Google factors into search ranking, alongside mobile-friendliness, security, and absence of